# Far Out: Predicting Long-Term Human Mobility

Save this PDF as:

Size: px
Start display at page:

## Transcription

3 Lat Lat Lat 22 Lat 23 Lon Lon Lon Lon Sun Mon Fri Sat Figure 3: Our continuous vector representation of a day d consists of the median latitude and longitude for each hour of the day (: through 23:59), binary encoding of the day of week, and a binary feature signifying whether a national holiday falls on d. 2 : - :59 23: - 23:59 Other 2 Other Sun Mon Hol. Fri Sat Hol. Figure 4: Our cell-based vector representation of a day d encodes the probability distribution over dominant cells conditioned on the time within d, and the same day-of-week and holiday information as the continuous representation (last 8 elements). time of day, and day type, respectively. Ω C is the set of all cells. We construct a feature vector for each day from this probability distribution as shown in Fig. 4, where the first elements model the occupancy probability for the discrete places between : and :59 of the day, the next elements capture : through :59, etc. The final 8 elements are identical to those in the continuous representation. The discretized representation sacrifices the potential precision of the continuous representation for a richer representation of uncertainty. It does not constrain the subject s location to a single location or cell, but instead represents the fact that the subject could be in one of several cells with some uncertainty for each one. The decision to divide the data into 24-hour segments is not arbitrary. Applying DFT to the raw GPS data as described above shows that most of the energy is concentrated in periods shorter or equal to 24 hours. Now we turn our attention to the eigenanalysis of the subjects location, which provides further insights into the data. Each subject is represented by a matrix D, where each row is a day (either in the continuous or the cell form). Prior to computing PCA, we apply Mercator cylindrical projection on the GPS data and normalize each column of observations by subtracting out its mean µ and dividing by its standard deviation σ. Normalizing with the mean and standard deviation scales the data so values in each column are in approximately the same range, which in turn prevents any columns from dominating the principal components. Applying SVD, we effectively find a set of eigenvectors of D s covariance matrix, which we call eigendays (Fig. 5).A few top eigendays with the largest eigenvalues induce a subspace, onto which a day can be projected, and that captures most of the variance in the data. For virtually all subjects, ten eigendays are enough to reconstruct their entire location log with more than 9% accuracy. In other words, we can accurately compress an arbitrary day d into only n! d weights w,, w n that induce a weighted sum over a common set of ten most dominant eigendays E i : «ff nÿ d w i E i ` µ diagpσq. (3) i This applies to both continuous and discretized data. The reason for this is that human mobility is relatively regular, and there is a large amount of redundancy in the raw representation of people s location. Note that unlike most other approaches, such as Markov models, PCA captures longterm correlations in the data. In our case, this means patterns in location over an entire day, as well as joint correlations among additional attributes (day of week, holiday) and the locations. Our eigenanalysis shows that there are strong correlations among a subject s latitudes and longitudes over time, and also correlations between other features, such as the dayof-week, and raw location. Let s take eigenday #2 (E 2 ) in Fig. 5 as an example. From the last 8 elements, we see that PCA automatically grouped holidays, weekends, and Tuesdays within this eigenday. The location pattern for days that fit these criteria is shown in the first 48 elements. In particular, E 2 makes it evident that this person spends her evenings and nights (from 6: to 24:) at a particular constant location in the North-West corner of her data, which turns out to be her home. The last 8 elements of each eigenday can be viewed as indicators that show how strongly the location patterns in the rest of the corresponding eigenday exhibit themselves on a given day-of-week ˆ holiday combination. For instance, E 3 is dominant on Saturdays, E 7 on Fridays, and E on Tuesdays that are not holidays (compare with E 2 ). Fig. 6 shows the top ten eigendays for the cell-based representation. Now we see patterns in terms of probability distributions over significant cells. For instance, this subject exhibits a strong baseline behavior (E ) on all days and especially nonworking days except for Tuesdays, which are captured in E 2. Note that the complex patterns in cell occupancy as well as the associated day types can be directly read off the eigendays. Our eigenday decomposition is also useful for detection of anomalous behavior. Given a set of eigendays and their typical weights computed from training data, we can compute how much a new day deviates from the subspace formed by the historical eigendays. The larger the deviation, the more atypical the day is. We leave this opportunity for future work. So far we have been focusing on the descriptive aspect of our models what types of patterns they extract and how can we interpret them. Now we turn to the predictive power of Far Out. Predictive Models We consider three general types of models for long-term location prediction. Each type works with both continuous (raw GPS) as well as discretized (triangular cells) data, and all our models are directly applied to both types of data without any modification of the learning process. Furthermore, while we experiment with two observed features (day

4 Eigenday # Eigenday # Eigenday # Eigenday # Eigenday #9 Eigenday # Eigenday # Eigenday # Eigenday # Eigenday # Eigenday # Eigenday #2 Figure 5: 8 Visualization 6 22 SMTWT of the F SHtop ten most 8 dominant 6 22 eigendays SMTWT F SH (E.3 through.2.e ). The. leftmost elements.2. of. each.2 eigenday correspond to the latitude and longitude over the 24 hours of a.6 Eigenday #3 Eigenday #4 day, latitude plotted 8 in6the top 22 SMTWT rows, F SH longitude in8 the bottom. 6 22The SMTWTnext F SH 7 binary.2 slots. capture. the.2 seven.3 days.2 of a week,.2 and the.4 last.6ele- ment models holidays versus regular days (cf. Fig. 3). The Eigenday #5 Eigenday #6 patterns in the GPS8 as well 6 as 22 the SMTWT calendar F SH features 8are color-coded 6 22 SMTWT using F SH the.2 mapping.2 shown.4 below.6 each.8 eigenday s s Eigenday # Eigenday # Eigenday # Eigenday # Eigenday # Eigenday # Figure 6: Visualization of the top six most dominant eigendays (E through E 6). The larger matrix within an eigenday shows cell occupancy patterns over the 24 hours of a day. Patterns in the calendar segment of each eigenday are shown below each matrix (cf. Fig. 4). of week and holiday), our models can handle arbitrary number of additional features, such as season, predicted weather, social and political features, known traffic conditions, information extracted from the power spectrum of an individual, and other calendar features (e.g., Is this the second Thursday of a month?; Does a concert or a conference take place?). In the course of eigendecomposition, Far Out automatically eliminates insignificant and redundant features. Mean Day Baseline Model For the continuous GPS representation, the baseline model calculates the average latitude and longitude for each hour of day for each day type. In the discrete case, we use the mode of cell IDs instead of the average. To make a prediction for a query with certain observed features o, this model simply retrieves all days that match o from the training data, and outputs their mean or mode. Although simple, this baseline is quite powerful, especially on large datasets such as ours. It virtually eliminates all random noise for repeatedly visited places. Additionally, since the spatial distribution of sporadic and unpredictable trips is largely symmetric over long periods of time, the errors these trips would have caused tend to be averaged out by this model (e.g., a spontaneous trip Seattle-Las Vegas is balanced by an isolated flight Seattle-Alaska). Projected Eigendays Model First, we learn all principal components (a.k.a. eigendays) from the training data as described above. This results in a n ˆ n matrix P, with eigendays as columns, where n is the dimensionality of the original representation of each day (either 56 or 272). At testing time, we want to find a fitting vector of weights w, such that the observed part of the query can be represented as a weighted sum of the corresponding elements of the principal components in matrix P. More specifically, this model predicts a subject s location at a particular time t q in the future by the following process. First, we extract observed features from t q, such as which day of week t q corresponds to.the observed feature values are then written into a query vector q. Now we project q onto the eigenday space using only the observed elements of the eigendays. This yields a weight for each eigenday, that captures how dominant that eigenday is given the observed feature values: w pq µq diagpσ qp c (4) where q is a row vector of length m (the number of observed elements in the query vector), P c is a m ˆ c matrix (c is the number of principal components considered), and w is a row vector of length c. Since we implement PCA in the space of normalized variables, we need to normalize the query vector as well. This is achieved by subtracting the mean µ, and component-wise division by the variance of each column σ. Note that finding an optimal set of weights can be viewed as solving (for w) a system of linear equations given by wp T c pq µq diagpσ q. (5) However, under most circumstances, such a system is illconditioned, which leads to an undesirable numerical sensitivity and subsequently poor results. The system is either over- or under-determined, except when c m. Furthermore, P T c may be singular. Theorem. The projected eigendays model learns weights by performing a least-squares fit. Proof. If P has linearly independent rows, a generalized inverse (e.g., Moore-Penrose) is given by P ` P pp P q (Ben-Israel and Greville 23). In our case, P P R mˆc and by definition forms an orthonormal basis. Therefore P P is an identity matrix and it follows that P ` P T. It is known that pseudoinverse provides a leastsquares solution to a system of linear equations (Penrose 956). Thus, equations 4 and 5 are theoretically equivalent, but the earlier formulation is significantly more elegant, efficient, and numerically stable. Using Eq. 3, the inferred weights are subsequently used to generate the prediction (either continuous GPS or probability distribution over cells) for time t q. Note that both training

6 (a) Mean Baseline (b) PCA Cumulative (c) PCA Separate Figure 9: How test error varies depending on how far into the future we predict and how much training data we use. Each plot shows the prediction error, in km, as a function of the amount of training data in weeks (vertical axes), and how many weeks into the future the models predict (horizontal axes). Plots (a) and (b) visualize cumulative error, where a pixel with coordinates px, yq represents the average error over testing weeks through x, when learning on training weeks through y. Plot (c) shows, on a log scale, the error for each pair of weeks separately, where we train only on week y and test on x. training data, the error decreases, especially for extremely long-term predictions. Fig. 9c shows that not all weeks are created equal. There are several unusual and therefore difficult weeks (e.g., test week #38), but in general our approach achieves high accuracy even for predictions 8 weeks into the future. Subsequent work can take advantage of the hindsight afforded by Fig. 9, and eliminate anomalous or confusing time periods (e.g., week #3) from the training set. Finally, decomposition of the prediction error along day types shows that for human subjects, weekends are most difficult to predict, whereas work days are least entropic. While this is to be expected, we notice a more interesting pattern, where the further away a day is from a nonworking day, the more predictable it is. For instance, Wednesdays in a regular week are the easiest, Fridays and Mondays are harder, and weekends are most difficult. This motif is evident across all human subjects and across a number of metrics, including location entropy, KL divergence and accuracy (cell-based representation), as well as absolute error (continuous data). Shuttles and paratransit exhibit the exact inverse of this pattern. Related Work There is ample previous work on building models of shortterm mobility, both individual and aggregate, descriptive as well as predictive. But there is a gap in modeling and predicting long-term mobility, which is our contribution (see Table ). Recent research has shown that surprisingly rich models of human behavior can be learned from GPS data alone, for example (Ashbrook and Starner 23; Liao, Fox, and Kautz 25; Krumm and Horvitz 26; Ziebart et al. 28; Sadilek and Kautz 2). However, previous work focused on making predictions at fixed, and relatively short, time scales. Consequently, questions such as Where is Karl go- Short Term Long Term Descriptive Previous work Previous work Predictive Previous work Only Far Out Unified Previous work Only Far Out Table : The context of our contributions. ing to be in the next hour? can often be answered with high accuracy. By contrast, this work explores the predictability of people s mobility at various temporal scales, and specifically far into the future. While short-term prediction is often sufficient for routing in wireless networks, one of the major applications of location modeling to date, long-term modeling is crucial in ubiquitous computing, infrastructure planning, traffic prediction, and other areas, as discussed in the introduction. Much effort on the descriptive models has been motivated by the desire to extract patterns of human mobility, and subsequently leverage them in simulations that accurately mimic observed general statistics of real trajectories (Kim, Kotz, and Kim 26; González, Hidalgo, and Barabási 28; Lee et al. 29; Li et al. 2; Kim and Kotz 2). However, all these works focus on aggregate behavior and do not address the problem of location prediction, which is the primary focus of this paper. Virtually all predictive models published to date have addressed only short-term location prediction. Even works with specific long-term focus have considered only predictions up to hours into the future (Scellato et al. 2). Furthermore, each proposed approach has been specifically tailored for either continuous or discrete data, but not both. For example, (Eagle and Pentland 29) consider only four discrete locations and make predictions up to 2 hours into the future. By contrast, this paper presents a general model for short- as well as long-term (scale of months and years) prediction, capable of working with both types of data representation. Jeung et al. (28) evaluate a hybrid location model that invokes two different prediction algorithms, one for queries that are temporally close, and the other for predictions further into the future. However, their approach requires selecting a large number of parameters and metrics. Additionally, Jeung et al. experiment with mostly synthetic data. By contrast, we present a unified and nonparametric mobility model and evaluate on an extensive dataset recorded entirely by real-world sensors. The recent surge of online social networks sparked interest in predicting people s location from their online behavior and interactions (Cho, Myers, and Leskovec 2; Sadilek, Kautz, and Bigham 22). However, unlike our work, they address short-term prediction on very sparsely sampled location data, where user location is recorded only when she posts a status update. In the realm of long-term prediction, (Krumm and Brush 2) model the probability of being at home at any given hour of a day.we focus on capturing long-term correlations and patterns in the data, and our models handle a large (or even unbounded, in our continuous representation) number

### Similarity Search and Mining in Uncertain Spatial and Spatio Temporal Databases. Andreas Züfle

Similarity Search and Mining in Uncertain Spatial and Spatio Temporal Databases Andreas Züfle Geo Spatial Data Huge flood of geo spatial data Modern technology New user mentality Great research potential

### Home Heating Using GPS-Based Arrival Prediction

Home Heating Using GPS-Based Arrival Prediction James Scott, John Krumm, Brian Meyers, A.J. Brush and Ashish Kapoor Microsoft Research {jws, jckrumm, brianme, ajbrush, akapoor}@microsoft.com Abstract.

### Estimation of Human Mobility Patterns and Attributes Analyzing Anonymized Mobile Phone CDR:

Estimation of Human Mobility Patterns and Attributes Analyzing Anonymized Mobile Phone CDR: Developing Real-time Census from Crowds of Greater Dhaka Ayumi Arai 1 and Ryosuke Shibasaki 1,2 1 Department

### Nonlinear Iterative Partial Least Squares Method

Numerical Methods for Determining Principal Component Analysis Abstract Factors Béchu, S., Richard-Plouet, M., Fernandez, V., Walton, J., and Fairley, N. (2016) Developments in numerical treatments for

### APPM4720/5720: Fast algorithms for big data. Gunnar Martinsson The University of Colorado at Boulder

APPM4720/5720: Fast algorithms for big data Gunnar Martinsson The University of Colorado at Boulder Course objectives: The purpose of this course is to teach efficient algorithms for processing very large

### Face Recognition using Principle Component Analysis

Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA Summary This is the summary of the basic idea about PCA

### An Order-Invariant Time Series Distance Measure [Position on Recent Developments in Time Series Analysis]

An Order-Invariant Time Series Distance Measure [Position on Recent Developments in Time Series Analysis] Stephan Spiegel and Sahin Albayrak DAI-Lab, Technische Universität Berlin, Ernst-Reuter-Platz 7,

### The Scientific Data Mining Process

Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

### BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 123 CHAPTER 7 BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 7.1 Introduction Even though using SVM presents

### Component Ordering in Independent Component Analysis Based on Data Power

Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals

### IDENTIFICATION OF KEY LOCATIONS BASED ON ONLINE SOCIAL NETWORK ACTIVITY

H. Efstathiades, D. Antoniades, G. Pallis, M. D. Dikaiakos IDENTIFICATION OF KEY LOCATIONS BASED ON ONLINE SOCIAL NETWORK ACTIVITY 1 Motivation Key Locations information is of high importance for various

### Data, Measurements, Features

Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are

### Recommendations in Mobile Environments. Professor Hui Xiong Rutgers Business School Rutgers University. Rutgers, the State University of New Jersey

1 Recommendations in Mobile Environments Professor Hui Xiong Rutgers Business School Rutgers University ADMA-2014 Rutgers, the State University of New Jersey Big Data 3 Big Data Application Requirements

### A Introduction to Matrix Algebra and Principal Components Analysis

A Introduction to Matrix Algebra and Principal Components Analysis Multivariate Methods in Education ERSH 8350 Lecture #2 August 24, 2011 ERSH 8350: Lecture 2 Today s Class An introduction to matrix algebra

### Using the Singular Value Decomposition

Using the Singular Value Decomposition Emmett J. Ientilucci Chester F. Carlson Center for Imaging Science Rochester Institute of Technology emmett@cis.rit.edu May 9, 003 Abstract This report introduces

### Lecture 2: Descriptive Statistics and Exploratory Data Analysis

Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals

### EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set

EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set Amhmed A. Bhih School of Electrical and Electronic Engineering Princy Johnson School of Electrical and Electronic Engineering Martin

### Predict the Popularity of YouTube Videos Using Early View Data

000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050

### Sanjeev Kumar. contribute

RESEARCH ISSUES IN DATAA MINING Sanjeev Kumar I.A.S.R.I., Library Avenue, Pusa, New Delhi-110012 sanjeevk@iasri.res.in 1. Introduction The field of data mining and knowledgee discovery is emerging as a

### MOBILITY DATA MODELING AND REPRESENTATION

PART I MOBILITY DATA MODELING AND REPRESENTATION 1 Trajectories and Their Representations Stefano Spaccapietra, Christine Parent, and Laura Spinsanti 1.1 Introduction For a long time, applications have

### Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication

Time Domain and Frequency Domain Techniques For Multi Shaker Time Waveform Replication Thomas Reilly Data Physics Corporation 1741 Technology Drive, Suite 260 San Jose, CA 95110 (408) 216-8440 This paper

### Big Data Analytics in Mobile Environments

1 Big Data Analytics in Mobile Environments 熊 辉 教 授 罗 格 斯 - 新 泽 西 州 立 大 学 2012-10-2 Rutgers, the State University of New Jersey Why big data: historical view? Productivity versus Complexity (interrelatedness,

### Principal Component Analysis Application to images

Principal Component Analysis Application to images Václav Hlaváč Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics Center for Machine Perception http://cmp.felk.cvut.cz/

### Music Mood Classification

Music Mood Classification CS 229 Project Report Jose Padial Ashish Goel Introduction The aim of the project was to develop a music mood classifier. There are many categories of mood into which songs may

### Image Compression through DCT and Huffman Coding Technique

International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul

### A STUDY REGARDING INTER DOMAIN LINKED DOCUMENTS SIMILARITY AND THEIR CONSEQUENT BOUNCE RATE

STUDIA UNIV. BABEŞ BOLYAI, INFORMATICA, Volume LIX, Number 1, 2014 A STUDY REGARDING INTER DOMAIN LINKED DOCUMENTS SIMILARITY AND THEIR CONSEQUENT BOUNCE RATE DIANA HALIŢĂ AND DARIUS BUFNEA Abstract. Then

### An Analysis of the Transitions between Mobile Application Usages based on Markov Chains

An Analysis of the Transitions between Mobile Application Usages based on Markov Chains Charles Gouin-Vallerand LICEF Research Center, Télé- Université du Québec 5800 St-Denis Boul. Montreal, QC H2S 3L5

### CHAPTER VII CONCLUSIONS

CHAPTER VII CONCLUSIONS To do successful research, you don t need to know everything, you just need to know of one thing that isn t known. -Arthur Schawlow In this chapter, we provide the summery of the

### CNFSAT: Predictive Models, Dimensional Reduction, and Phase Transition

CNFSAT: Predictive Models, Dimensional Reduction, and Phase Transition Neil P. Slagle College of Computing Georgia Institute of Technology Atlanta, GA npslagle@gatech.edu Abstract CNFSAT embodies the P

### CS 591.03 Introduction to Data Mining Instructor: Abdullah Mueen

CS 591.03 Introduction to Data Mining Instructor: Abdullah Mueen LECTURE 3: DATA TRANSFORMATION AND DIMENSIONALITY REDUCTION Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major

### Multivariate Analysis of Ecological Data

Multivariate Analysis of Ecological Data MICHAEL GREENACRE Professor of Statistics at the Pompeu Fabra University in Barcelona, Spain RAUL PRIMICERIO Associate Professor of Ecology, Evolutionary Biology

### Review Jeopardy. Blue vs. Orange. Review Jeopardy

Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 0-3 Jeopardy Round \$200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?

### Continuous Time Bayesian Networks for Inferring Users Presence and Activities with Extensions for Modeling and Evaluation

Continuous Time Bayesian Networks for Inferring Users Presence and Activities with Extensions for Modeling and Evaluation Uri Nodelman 1 Eric Horvitz Microsoft Research One Microsoft Way Redmond, WA 98052

### Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning

Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step

### Preventing Denial-of-request Inference Attacks in Location-sharing Services

Preventing Denial-of-request Inference Attacks in Location-sharing Services Kazuhiro Minami Institute of Statistical Mathematics, Tokyo, Japan Email: kminami@ism.ac.jp Abstract Location-sharing services

### Least-Squares Intersection of Lines

Least-Squares Intersection of Lines Johannes Traa - UIUC 2013 This write-up derives the least-squares solution for the intersection of lines. In the general case, a set of lines will not intersect at a

### Soft Clustering with Projections: PCA, ICA, and Laplacian

1 Soft Clustering with Projections: PCA, ICA, and Laplacian David Gleich and Leonid Zhukov Abstract In this paper we present a comparison of three projection methods that use the eigenvectors of a matrix

### By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

This material is posted here with permission of the IEEE Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services Internal

### Predicting Mobile Application Usage Using Contextual Information

Predicting Mobile Application Usage Using Contextual Information Ke Huang khuang@cs.uml.edu Chunhui Zhang czhang@cs.uml.edu ABSTRACT As the mobile applications become increasing popular, people are installing

### Behavioral Entropy of a Cellular Phone User

Behavioral Entropy of a Cellular Phone User Santi Phithakkitnukoon 1, Husain Husna, and Ram Dantu 3 1 santi@unt.edu, Department of Comp. Sci. & Eng., University of North Texas hjh36@unt.edu, Department

### Data Mining Project Report. Document Clustering. Meryem Uzun-Per

Data Mining Project Report Document Clustering Meryem Uzun-Per 504112506 Table of Content Table of Content... 2 1. Project Definition... 3 2. Literature Survey... 3 3. Methods... 4 3.1. K-means algorithm...

### Keywords: Mobility Prediction, Location Prediction, Data Mining etc

Volume 4, Issue 4, April 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Data Mining Approach

### Mining Signatures in Healthcare Data Based on Event Sequences and its Applications

Mining Signatures in Healthcare Data Based on Event Sequences and its Applications Siddhanth Gokarapu 1, J. Laxmi Narayana 2 1 Student, Computer Science & Engineering-Department, JNTU Hyderabad India 1

### Text Mining in JMP with R Andrew T. Karl, Senior Management Consultant, Adsurgo LLC Heath Rushing, Principal Consultant and Co-Founder, Adsurgo LLC

Text Mining in JMP with R Andrew T. Karl, Senior Management Consultant, Adsurgo LLC Heath Rushing, Principal Consultant and Co-Founder, Adsurgo LLC 1. Introduction A popular rule of thumb suggests that

### Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data

CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear

### Bayesian Statistics: Indian Buffet Process

Bayesian Statistics: Indian Buffet Process Ilker Yildirim Department of Brain and Cognitive Sciences University of Rochester Rochester, NY 14627 August 2012 Reference: Most of the material in this note

### Automatic Web Page Classification

Automatic Web Page Classification Yasser Ganjisaffar 84802416 yganjisa@uci.edu 1 Introduction To facilitate user browsing of Web, some websites such as Yahoo! (http://dir.yahoo.com) and Open Directory

### ADVANCED LINEAR ALGEBRA FOR ENGINEERS WITH MATLAB. Sohail A. Dianat. Rochester Institute of Technology, New York, U.S.A. Eli S.

ADVANCED LINEAR ALGEBRA FOR ENGINEERS WITH MATLAB Sohail A. Dianat Rochester Institute of Technology, New York, U.S.A. Eli S. Saber Rochester Institute of Technology, New York, U.S.A. (g) CRC Press Taylor

### Integration of GPS Traces with Road Map

Integration of GPS Traces with Road Map Lijuan Zhang Institute of Cartography and Geoinformatics Leibniz University of Hannover Hannover, Germany +49 511.762-19437 Lijuan.Zhang@ikg.uni-hannover.de Frank

### Real Time Traffic Monitoring With Bayesian Belief Networks

Real Time Traffic Monitoring With Bayesian Belief Networks Sicco Pier van Gosliga TNO Defence, Security and Safety, P.O.Box 96864, 2509 JG The Hague, The Netherlands +31 70 374 02 30, sicco_pier.vangosliga@tno.nl

### Statistical Forecasting of High-Way Traffic Jam at a Bottleneck

Metodološki zvezki, Vol. 9, No. 1, 2012, 81-93 Statistical Forecasting of High-Way Traffic Jam at a Bottleneck Igor Grabec and Franc Švegl 1 Abstract Maintenance works on high-ways usually require installation

### A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication

2012 45th Hawaii International Conference on System Sciences A New Ensemble Model for Efficient Churn Prediction in Mobile Telecommunication Namhyoung Kim, Jaewook Lee Department of Industrial and Management

### The Optimality of Naive Bayes

The Optimality of Naive Bayes Harry Zhang Faculty of Computer Science University of New Brunswick Fredericton, New Brunswick, Canada email: hzhang@unbca E3B 5A3 Abstract Naive Bayes is one of the most

### Biostatistics: A QUICK GUIDE TO THE USE AND CHOICE OF GRAPHS AND CHARTS

Biostatistics: A QUICK GUIDE TO THE USE AND CHOICE OF GRAPHS AND CHARTS 1. Introduction, and choosing a graph or chart Graphs and charts provide a powerful way of summarising data and presenting them in

### Cheng Soon Ong & Christfried Webers. Canberra February June 2016

c Cheng Soon Ong & Christfried Webers Research Group and College of Engineering and Computer Science Canberra February June (Many figures from C. M. Bishop, "Pattern Recognition and ") 1of 31 c Part I

### CS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing

CS Master Level Courses and Areas The graduate courses offered may change over time, in response to new developments in computer science and the interests of faculty and students; the list of graduate

### Machine Learning and Pattern Recognition Logistic Regression

Machine Learning and Pattern Recognition Logistic Regression Course Lecturer:Amos J Storkey Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Crichton Street,

### Social Media Mining. Data Mining Essentials

Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

### CS 5614: (Big) Data Management Systems. B. Aditya Prakash Lecture #18: Dimensionality Reduc7on

CS 5614: (Big) Data Management Systems B. Aditya Prakash Lecture #18: Dimensionality Reduc7on Dimensionality Reduc=on Assump=on: Data lies on or near a low d- dimensional subspace Axes of this subspace

### Marketing Mix Modelling and Big Data P. M Cain

1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored

### Spatial Data Analysis

14 Spatial Data Analysis OVERVIEW This chapter is the first in a set of three dealing with geographic analysis and modeling methods. The chapter begins with a review of the relevant terms, and an outlines

### Advanced Spatial Statistics Fall 2012 NCSU. Fuentes Lecture notes

Advanced Spatial Statistics Fall 2012 NCSU Fuentes Lecture notes Areal unit data 2 Areal Modelling Areal unit data Key Issues Is there spatial pattern? Spatial pattern implies that observations from units

### The primary goal of this thesis was to understand how the spatial dependence of

5 General discussion 5.1 Introduction The primary goal of this thesis was to understand how the spatial dependence of consumer attitudes can be modeled, what additional benefits the recovering of spatial

### A Survey on Outlier Detection Techniques for Credit Card Fraud Detection

IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661, p- ISSN: 2278-8727Volume 16, Issue 2, Ver. VI (Mar-Apr. 2014), PP 44-48 A Survey on Outlier Detection Techniques for Credit Card Fraud

### Support Vector Machines with Clustering for Training with Very Large Datasets

Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano

### Clustering Time Series Based on Forecast Distributions Using Kullback-Leibler Divergence

Clustering Time Series Based on Forecast Distributions Using Kullback-Leibler Divergence Taiyeong Lee, Yongqiao Xiao, Xiangxiang Meng, David Duling SAS Institute, Inc 100 SAS Campus Dr. Cary, NC 27513,

### REROUTING VOICE OVER IP CALLS BASED IN QOS

1 REROUTING VOICE OVER IP CALLS BASED IN QOS DESCRIPTION BACKGROUND OF THE INVENTION 1.- Field of the invention The present invention relates to telecommunications field. More specifically, in the contextaware

### Image Compression Effects on Face Recognition for Images with Reduction in Size

Image Compression Effects on Face Recognition for Images with Reduction in Size Padmaja.V.K Jawaharlal Nehru Technological University, Anantapur Giri Prasad, PhD. B. Chandrasekhar, PhD. ABSTRACT In this

### PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

### Real Time Bus Monitoring System by Sharing the Location Using Google Cloud Server Messaging

Real Time Bus Monitoring System by Sharing the Location Using Google Cloud Server Messaging Aravind. P, Kalaiarasan.A 2, D. Rajini Girinath 3 PG Student, Dept. of CSE, Anand Institute of Higher Technology,

### 1 Example of Time Series Analysis by SSA 1

1 Example of Time Series Analysis by SSA 1 Let us illustrate the 'Caterpillar'-SSA technique [1] by the example of time series analysis. Consider the time series FORT (monthly volumes of fortied wine sales

### Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

### Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP

Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP ABSTRACT In data mining modelling, data preparation

### RESEARCH STATEMENT. Guohua Liu

RESEARCH STATEMENT 1 Introduction My research is focused on computational inference that concerns effective problem solving via inference with data or knowledge. Constraint programming, mathematical programming,

### A Learning Based Method for Super-Resolution of Low Resolution Images

A Learning Based Method for Super-Resolution of Low Resolution Images Emre Ugur June 1, 2004 emre.ugur@ceng.metu.edu.tr Abstract The main objective of this project is the study of a learning based method

### Information Management course

Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)

### A Genetic Algorithm-Evolved 3D Point Cloud Descriptor

A Genetic Algorithm-Evolved 3D Point Cloud Descriptor Dominik Wȩgrzyn and Luís A. Alexandre IT - Instituto de Telecomunicações Dept. of Computer Science, Univ. Beira Interior, 6200-001 Covilhã, Portugal

### Context-aware taxi demand hotspots prediction

Int. J. Business Intelligence and Data Mining, Vol. 5, No. 1, 2010 3 Context-aware taxi demand hotspots prediction Han-wen Chang, Yu-chin Tai and Jane Yung-jen Hsu* Department of Computer Science and Information

### BayesX - Software for Bayesian Inference in Structured Additive Regression

BayesX - Software for Bayesian Inference in Structured Additive Regression Thomas Kneib Faculty of Mathematics and Economics, University of Ulm Department of Statistics, Ludwig-Maximilians-University Munich

### TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM Thanh-Nghi Do College of Information Technology, Cantho University 1 Ly Tu Trong Street, Ninh Kieu District Cantho City, Vietnam

### Medical Information Management & Mining. You Chen Jan,15, 2013 You.chen@vanderbilt.edu

Medical Information Management & Mining You Chen Jan,15, 2013 You.chen@vanderbilt.edu 1 Trees Building Materials Trees cannot be used to build a house directly. How can we transform trees to building materials?

### A Comparison Framework of Similarity Metrics Used for Web Access Log Analysis

A Comparison Framework of Similarity Metrics Used for Web Access Log Analysis Yusuf Yaslan and Zehra Cataltepe Istanbul Technical University, Computer Engineering Department, Maslak 34469 Istanbul, Turkey

### Data Clustering. Dec 2nd, 2013 Kyrylo Bessonov

Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms k-means Hierarchical Main

### Learning Gaussian process models from big data. Alan Qi Purdue University Joint work with Z. Xu, F. Yan, B. Dai, and Y. Zhu

Learning Gaussian process models from big data Alan Qi Purdue University Joint work with Z. Xu, F. Yan, B. Dai, and Y. Zhu Machine learning seminar at University of Cambridge, July 4 2012 Data A lot of

### Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012

Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts

### Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

### Chapter 7. Lyapunov Exponents. 7.1 Maps

Chapter 7 Lyapunov Exponents Lyapunov exponents tell us the rate of divergence of nearby trajectories a key component of chaotic dynamics. For one dimensional maps the exponent is simply the average

### Tensor Methods for Machine Learning, Computer Vision, and Computer Graphics

Tensor Methods for Machine Learning, Computer Vision, and Computer Graphics Part I: Factorizations and Statistical Modeling/Inference Amnon Shashua School of Computer Science & Eng. The Hebrew University

### Models for Product Demand Forecasting with the Use of Judgmental Adjustments to Statistical Forecasts

Page 1 of 20 ISF 2008 Models for Product Demand Forecasting with the Use of Judgmental Adjustments to Statistical Forecasts Andrey Davydenko, Professor Robert Fildes a.davydenko@lancaster.ac.uk Lancaster

### A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data

A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data Athanasius Zakhary, Neamat El Gayar Faculty of Computers and Information Cairo University, Giza, Egypt

### Accurate and robust image superresolution by neural processing of local image representations

Accurate and robust image superresolution by neural processing of local image representations Carlos Miravet 1,2 and Francisco B. Rodríguez 1 1 Grupo de Neurocomputación Biológica (GNB), Escuela Politécnica

### Subspace Analysis and Optimization for AAM Based Face Alignment

Subspace Analysis and Optimization for AAM Based Face Alignment Ming Zhao Chun Chen College of Computer Science Zhejiang University Hangzhou, 310027, P.R.China zhaoming1999@zju.edu.cn Stan Z. Li Microsoft

: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary

Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus 1. Introduction Facebook is a social networking website with an open platform that enables developers to extract and utilize user information

### Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

### A Logistic Regression Approach to Ad Click Prediction

A Logistic Regression Approach to Ad Click Prediction Gouthami Kondakindi kondakin@usc.edu Satakshi Rana satakshr@usc.edu Aswin Rajkumar aswinraj@usc.edu Sai Kaushik Ponnekanti ponnekan@usc.edu Vinit Parakh

### Detecting Human Behavior Patterns from Mobile Phone

Journal of Computational Information Systems 8: 6 (2012) 2671 2679 Available at http://www.jofcis.com Detecting Human Behavior Patterns from Mobile Phone Anqin ZHANG 1,2,, Wenjun YE 2, Yuan PENG 1,2 1