Temporal Data Mining for Small and Big Data. Theophano Mitsa, Ph.D. Independent Data Mining/Analytics Consultant
|
|
- Shon Morton
- 8 years ago
- Views:
Transcription
1 Temporal Data Mining for Small and Big Data Theophano Mitsa, Ph.D. Independent Data Mining/Analytics Consultant
2 What is Temporal Data Mining? Knowledge discovery in data that contain temporal information. Two types of time data: -event data (i.e., time of purchase) -time series data (EKG data). Talk Outline A. General Concepts B. Temporal Data Mining Applications: Medicine, bioinformatics, spatiotemporal data C. Temporal Data Mining and Big Data: business process, web data.
3 A. General Concepts
4 A.1 Time Data Representation and Temporality in Databases
5 Time Data Representation and Temporality in Databases Time series: Real-valued measurements at regular temporal intervals. Temporal sequences: Time stamped at regular or irregular time intervals. Example: The sequence of purchases of a customer on an online store. Transaction time: The time that information is entered in the database. For example, the time of a purchase. Valid time: The time an entity is valid in the real world. For example, the time the subscription of a customer starts. Bi-temporal time stamping. Have both a transaction and valid time.
6 Types of databases Snapshot databases: Keep the most recent version of data. Rollback databases: Support only a transaction time. Historical databases: Support only valid time. Temporal databases: Support both valid and transaction time. Allen s interval algebra Allen s interval algebra offers the most widely accepted way to express temporal relations and perform temporal reasoning [1]. Allen defines 13 temporal relations: before, after, meets, overlaps, etc.
7 Time Series Representation Requirements: Reduce the dimensionality of the similarity search problem, distance in the feature space less or equal in original space. Schemes: Fourier transform. Wavelet transform. Piecewise Aggregate Approximation and Piecewise Line Approximation. Shape Definition Language. Model-based, such as Hidden Markov model. Perceptually Important Points.
8 EKG PIP points
9 A.2. Temporal Data Mining Tasks Similarity Computation Classification/Clustering Pattern recognition Prediction
10 A.2.1 Time Series Similarity Computation
11 Similarity Computation in time series Distance-based. Dynamic Time Warping. This is applied when the time series are not aligned. Longest Common Subsequence. It assumes the same scale and baseline. It is tolerant to gaps and is more resistant to noise and outliers than DTW.
12 A.2.2 Classification/Clustering
13 Classification/Clustering of Time Series Data Non-model-based (traditional): Example: NNs, SVMs, decision trees, k-means. These can be applied to (a) features extracted from the series, such as PIPs, FT coefficients, trend, seasonality, mean, or (b) the raw time series data. Model-based. They use some model information about the time series, which comes from the fact that time series data values are usually correlated. Example: HMM, ARMA, AR, Markov chain.
14 A.2.3 Pattern Discovery in Temporal Data
15 Pattern Discovery Pattern discovery in event sequences: 1.Sequence mining (multiple sequences): Apriori, GSP.. 2. Association rule discovery (single sequence) 3. Frequent Episode Discovery (single sequence). An episode is a sequence of events appearing within a specific time window in a specific order, i.e., interest rates increase (event 1) and stock market drop (event 2). Pattern discovery in time series: 1. Motif and anomaly discovery (e.g., bioinformatics and computer networking monitoring). 2. Streaming data pattern discovery (e.g. financial data analysis or sensor data).
16 A.2.4 Prediction
17 Event prediction: Rare event prediction Prediction Event duration prediction: Regression Time Series Forecasting: Moving average Autoregression ARMA models
18 B. Applications B.1 Applications in Medicine
19 Chronus II Chronus II [3] is a temporal database mediator, that allows temporal abstractions. It extends the SQL language to allow general temporal queries on clinical databases for decisionsupport systems. On its basic level it uses Allen s interval algebra to define temporal relationships. A later ontological version [4] of the mediator exists that utilizes OWL and SWRL.
20 The TEMPADIS System This is a system for the discovery of patterns in courseof-disease data [5]. It was applied on a database of HIV patients. 18 variables were used, such as white blood cell count and drug types. Classification was performed in order to determine the health status of a patient. A decision tree approach was used. There were five health status categories ranging from asymptomatic to severe illness. Finally, the GSP algorithm was used for pattern detection in sequences of events across patients in the database.
21 Analysis and Classification of EEG Time Series In [6], the fractal dimension was used to analyze EEG signals and detect patterns. The fractal dimension was chosen because of the chaotic nature of the signals. In [7], 3 methods to classify EEG time series were compared. 1. Linear Discriminant Analysis. 2. Neural Networks. 3. Support Vector Machines. SVMs gave the best results.
22 B.2 Applications in Bioinformatics
23 General Concepts Microarray technology has enabled us to study thousands of genes simultaneously. This is done using gene expression profiles, which measure a gene s activity. Gene expression profiles can be obtained either at specific time points or at successive time intervals. In the second case, they are known as gene expression time series.
24 Clustering of Gene Expression Time Series Difficult problem because: Possible presence of noise, intersecting clusters. Time series are very short (even as short as four samples) Time series can be unevenly sampled. The time series could have different scaling and shifting. The similarity measure should be shape-based, i.e., it should be based on the changes in the intensity and not the intensity itself.
25 Clustering of Gene Expression Time Series Spline-Based Methods: can be used in time series with missing points. Model-Based Methods: For example, autoregressive equations or Hidden Markov Models can be used to model the series. Fuzzy-Clustering Based Methods Template-Based Methods: a template is used, after DTW is employed for alignment.
26 B.3 Spatiotemporal Applications
27 Analysis of moving point objects (MPOs) Two types of analysis: Descriptive modeling: Describe the entire lifeline of the moving object. Retrieval by content: Find a specific motion pattern. Descriptive Modeling Goal: Find clusters that describe the lifelines (movements). For example, the motion of a group of objects can be described using the motion azimuth. 1: the objects move in the same direction the objects move in perpendicular directions. 0: the objects move in opposite directions
28 MPO analysis: Retrieval by content Problem: Detect relative motion patterns, i.e. detect how the attributes of different object movements related over space and time (speed, change of speed, etc.) Main idea: Fit to the data a motion template with specific motion attributes Example patterns: Flocking: Objects within a circular area of radius moving in the same direction. Leadership: Objects moving in the same direction with one object being ahead of all other objects.
29 Trajectory Data Mining Problem: Find similar trajectories. This is an important problem ( e.g., object identification in video). The similarity measure must be able to handle: Different sampling rates, similar motions in different space regions, noise, data with different lengths. Approaches: LCSS [8], Minimum Bounding Rectangles [9], FT combined with SOM [10].
30 Open GeoDa Adds a Temporal Feature GeoDa [11] is a very popular open source tool for spatial analysis and modeling. In Sept. 12, it was announced that its new version will include space-time analysis maps, that will allow the user to track changes in spatial patterns over time, such as follow the change in the vegetation of an area.
31 C. Temporal Data Mining and Big Data
32 The 3 Vs of Big Data Variety Volume Velocity -> Real time/agile Analytics.
33 Agile Analytics In agile analytics, collective intelligence from the entire organization is used to develop continuously evolving prediction models as to how to enhance customer satisfaction and improve strategic business decisions.
34 C.1 Big Data and Business Processes
35 Value Chain Temporal Optimization Embedding of real-time fine granularity data in the business decision process: Real-time inventory management and efficient response to high demand times. Acquisition of real-time sensor data from the manufacturing process: - Manufacturing process efficiency: bottleneck identification, yield maximization, defect reduction. - X-raying [12] of business processes to ensure conformance with process design.
36 The Hospital and Agile Analytics Electronic medical records enable agile analytics. Possible uses: 1. Disease outbreak detection, with minimum latency. 2. Pharmacovigilance: Identification of drug adverse effects on a scale that is not possible in clinical trials.
37 C.2 Big Data from Web Usage Mining
38 Web Data Analysis for Behavioral Targeting Goals: Build behavior profiles for web users. At real-time, compute a relevance score for an ad that will decide or not the appearance of the ad. Data Mining Operations regarding users: Classification: Classify groups of users based on their profiles. Clustering: Used when the user categories are not known.
39 Mining the Web Usage Data Statistical analysis: For example, most frequently accessed web page, number of accessed web pages, maximum viewing time of a page, average length of a path to a site, etc. Path Analysis: paths. This yields the most frequent visited Association Rule Discovery: Discover the pages that are accessed together in a user session whose support exceeds a certain threshold. Sequential Pattern Discovery: Discover patterns that appear in a sequence of site visits by a user.
40 C.3 Big Data from Data Streams
41 Stream Pattern Discovery Algorithms Streaming data are of growing importance in many areas including monitoring for security purposes, financial forecasting, and analysis of location data. Challenges: 1. Huge amounts of data that arrive at high rates. 2. Often users need to respond immediately. Insight: The stream values are often correlated and a few hidden variables are enough to characterize the data.
42 Stream Pattern Discovery SPIRIT [13] : An algorithm that finds trends and hidden variables in a family of incoming streams. Main idea: Use Principle Component Analysis. Advantages : Adaptive,automatically detects changes in the incoming streams, scales linearly with the number of streams. 2. SpADe[14]: For the problem of matching an incoming stream against a predefined pattern: A warping distance that can handle shifting and scaling both in the amplitude and temporal dimensions. It can be incorporated in stream pattern discovery (in similarity search).
43 The AWSOM algorithm Purpose: For streaming data coming from sensors operating in hostile and remote environments. It allows sensors to detect patterns and trends [15]. Requirements of an algorithm that processes sensor stream data: Ability to detect simple or periodic patterns. Ability to filter out noise. Low memory usage. Be online and one pass. Ability to detect outliers. Should not require supervision by humans.
44 The AWSOM algorithm (continued) Main idea: The AWSOM algorithm utilizes wavelet primarily for the following reasons: (a) easy periodicity detection (b) need to store just a few coefficients (c) operates without supervision (d) requires only one pass. Experimental results showed that the algorithm can detect periodicities and bursts.
45 Conclusion Knowledge discovery in temporal data has applications in many areas. Since Big Data are temporal in nature, temporal data mining and especially real-time analytics and Agile Analytics are of increasing importance in order to understand the evolution of processes/customers in time and reduce the latency between data collection and using the data in decision making.
46 References 1. Allen, J. F., Maintaining Knowledge about Temporal Intervals, Communications of the ACM, vol. 26, no. 11, pp , Weiss, G.M. and H. Hirsch, Learning to Predict Rare Events in Event Sequences, Proceedings of the 4 th International Conference on Knowledge Discovery and Data Mining, pp , AAAI Press, O Connor, M.J., S.W. Tu, M.A. Musen, The Chronus II Temporal Database Mediator, Proceedings of the AMIA Annual Symposium, pp , San Antonio, TX, O Connor, M.J., R.D. Shankar, A.K.Das, An Ontology-Driven Mediator for Querying Time-Oriented Biomedical Data, 19 th IEEE International Symposium on Computer-Based Medical Systems, pp , Salt Lake City, Utah, Ramirez, J.C.G. et al., Temporal Pattern Discovery in Course-of- Disease Data, IEEE Engineering in Medicine and Biology, vol. 19, no. 4, pp , 2000.
47 References 6. Paramanathan, P. and R. Uthayakumar, Detecting Patterns in Irregular Time Series with Fractal Dimension, Proceedings of the International Conference on Computational Intelligence and Multimedia Applications, pp , [Gar03] Garrett, D. et al., Comparison of Linear, Non-Linear, and Feature Selection Methods for EEG Signal Classification, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp , June Vlachos, M., G. Kollios, D. Gunopoulos, Discovering Similar Multidimensional Trajectories, Proceedings of the International Conference on Data Engineering (ICDE), pp , Vlachos M., Hadjieleftheriou M., Gunopoulos D., Keogh E., Indexing Multi- Dimensional Time Series with Support for Multiple Distance Measures, Proceedings of the ACM SIGKDD Conference, pp , Washington DC (USA), August [Kha05] Khalid, S. and A. Naftel, Classifying Spatiotemporal Object Trajectories Using Unsupervised Learning of Basis Functions Coefficients, Proceedings of the 3 rd ACM International Workshop on Video Surveillance and Sensor Networks, pp , 2005.
48 References Van der Aalst, W., Process Mining, Communications of the ACM, pp , Papadimitriou, S., J. Sun, C. Faloutsos, Streaming Pattern Discovery in Multiple Time Series, Proceedings of the 31 st VLDB Conference, pp , Chen, Y. et al., SpADe: On Shape-Based Pattern Detection in Streaming Time Series, Proceedings of the IEEE 23 rd International Conference on Data Engineering, pp , Papadimitriou, S., A. Brockwell, C. Faloutsos, Adaptive, Unsupervised Stream Mining, The VLDB Journal, vol. 13, pp , 2004.
Introduction to Data Mining
Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:
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)
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
How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning
How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume
International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014
RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer
Introduction to Pattern Recognition
Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2009 CS 551, Spring 2009 c 2009, Selim Aksoy (Bilkent University)
Example application (1) Telecommunication. Lecture 1: Data Mining Overview and Process. Example application (2) Health
Lecture 1: Data Mining Overview and Process What is data mining? Example applications Definitions Multi disciplinary Techniques Major challenges The data mining process History of data mining Data mining
Data Mining Solutions for the Business Environment
Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over
How To Create A Data Science System
Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome Richard Breakiron Senior Director, Cyber Solutions Rbreakiron@vion.com Office: 571-353-6127 / Cell: 803-443-8002
Is a Data Scientist the New Quant? Stuart Kozola MathWorks
Is a Data Scientist the New Quant? Stuart Kozola MathWorks 2015 The MathWorks, Inc. 1 Facts or information used usually to calculate, analyze, or plan something Information that is produced or stored by
The Data Mining Process
Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data
Introduction to Data Mining
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing
Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition
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:
CHAPTER-24 Mining Spatial Databases
CHAPTER-24 Mining Spatial Databases 24.1 Introduction 24.2 Spatial Data Cube Construction and Spatial OLAP 24.3 Spatial Association Analysis 24.4 Spatial Clustering Methods 24.5 Spatial Classification
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,
An Overview of Knowledge Discovery Database and Data mining Techniques
An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,
REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc])
305 REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc]) (See also General Regulations) Any publication based on work approved for a higher degree should contain a reference
Process Mining. ^J Springer. Discovery, Conformance and Enhancement of Business Processes. Wil M.R van der Aalst Q UNIVERS1TAT.
Wil M.R van der Aalst Process Mining Discovery, Conformance and Enhancement of Business Processes Q UNIVERS1TAT m LIECHTENSTEIN Bibliothek ^J Springer Contents 1 Introduction I 1.1 Data Explosion I 1.2
Introduction. A. Bellaachia Page: 1
Introduction 1. Objectives... 3 2. What is Data Mining?... 4 3. Knowledge Discovery Process... 5 4. KD Process Example... 7 5. Typical Data Mining Architecture... 8 6. Database vs. Data Mining... 9 7.
A Case of Study on Hadoop Benchmark Behavior Modeling Using ALOJA-ML
www.bsc.es A Case of Study on Hadoop Benchmark Behavior Modeling Using ALOJA-ML Josep Ll. Berral, Nicolas Poggi, David Carrera Workshop on Big Data Benchmarks Toronto, Canada 2015 1 Context ALOJA: framework
ANALYTICS IN BIG DATA ERA
ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA MAURIZIO SALUSTI SAS Copyr i g ht 2012, SAS Ins titut
How To Cluster
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
Investigating Clinical Care Pathways Correlated with Outcomes
Investigating Clinical Care Pathways Correlated with Outcomes Geetika T. Lakshmanan, Szabolcs Rozsnyai, Fei Wang IBM T. J. Watson Research Center, NY, USA August 2013 Outline Care Pathways Typical Challenges
An Introduction to Data Mining
An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail
Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information
Continuous Fastest Path Planning in Road Networks by Mining Real-Time Traffic Event Information Eric Hsueh-Chan Lu Chi-Wei Huang Vincent S. Tseng Institute of Computer Science and Information Engineering
Machine Learning. 01 - Introduction
Machine Learning 01 - Introduction Machine learning course One lecture (Wednesday, 9:30, 346) and one exercise (Monday, 17:15, 203). Oral exam, 20 minutes, 5 credit points. Some basic mathematical knowledge
Big Data: Image & Video Analytics
Big Data: Image & Video Analytics How it could support Archiving & Indexing & Searching Dieter Haas, IBM Deutschland GmbH The Big Data Wave 60% of internet traffic is multimedia content (images and videos)
DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM
INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM M. Mayilvaganan 1, S. Aparna 2 1 Associate
Integrated Data Mining Strategy for Effective Metabolomic Data Analysis
The First International Symposium on Optimization and Systems Biology (OSB 07) Beijing, China, August 8 10, 2007 Copyright 2007 ORSC & APORC pp. 45 51 Integrated Data Mining Strategy for Effective Metabolomic
Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland
Data Mining and Knowledge Discovery in Databases (KDD) State of the Art Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland 1 Conference overview 1. Overview of KDD and data mining 2. Data
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
Mobile Phone APP Software Browsing Behavior using Clustering Analysis
Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Mobile Phone APP Software Browsing Behavior using Clustering Analysis
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
Principles of Data Mining by Hand&Mannila&Smyth
Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences
Learning outcomes. Knowledge and understanding. Competence and skills
Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges
Introduction to Engineering Using Robotics Experiments Lecture 17 Big Data
Introduction to Engineering Using Robotics Experiments Lecture 17 Big Data Yinong Chen 2 Big Data Big Data Technologies Cloud Computing Service and Web-Based Computing Applications Industry Control Systems
Adaptive Demand-Forecasting Approach based on Principal Components Time-series an application of data-mining technique to detection of market movement
Adaptive Demand-Forecasting Approach based on Principal Components Time-series an application of data-mining technique to detection of market movement Toshio Sugihara Abstract In this study, an adaptive
01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours.
(International Program) 01219141 Object-Oriented Modeling and Programming 3 (3-0) Object concepts, object-oriented design and analysis, object-oriented analysis relating to developing conceptual models
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
International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015
RESEARCH ARTICLE OPEN ACCESS Data Mining Technology for Efficient Network Security Management Ankit Naik [1], S.W. Ahmad [2] Student [1], Assistant Professor [2] Department of Computer Science and Engineering
Data Mining Analytics for Business Intelligence and Decision Support
Data Mining Analytics for Business Intelligence and Decision Support Chid Apte, T.J. Watson Research Center, IBM Research Division Knowledge Discovery and Data Mining (KDD) techniques are used for analyzing
Statistics for BIG data
Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before
OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP
Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key
PRACTICAL DATA MINING IN A LARGE UTILITY COMPANY
QÜESTIIÓ, vol. 25, 3, p. 509-520, 2001 PRACTICAL DATA MINING IN A LARGE UTILITY COMPANY GEORGES HÉBRAIL We present in this paper the main applications of data mining techniques at Electricité de France,
International Journal of World Research, Vol: I Issue XIII, December 2008, Print ISSN: 2347-937X DATA MINING TECHNIQUES AND STOCK MARKET
DATA MINING TECHNIQUES AND STOCK MARKET Mr. Rahul Thakkar, Lecturer and HOD, Naran Lala College of Professional & Applied Sciences, Navsari ABSTRACT Without trading in a stock market we can t understand
REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc])
299 REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc]) (See also General Regulations) Any publication based on work approved for a higher degree should contain a reference
Machine Learning with MATLAB David Willingham Application Engineer
Machine Learning with MATLAB David Willingham Application Engineer 2014 The MathWorks, Inc. 1 Goals Overview of machine learning Machine learning models & techniques available in MATLAB Streamlining the
TIETS34 Seminar: Data Mining on Biometric identification
TIETS34 Seminar: Data Mining on Biometric identification Youming Zhang Computer Science, School of Information Sciences, 33014 University of Tampere, Finland Youming.Zhang@uta.fi Course Description Content
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
Financial Trading System using Combination of Textual and Numerical Data
Financial Trading System using Combination of Textual and Numerical Data Shital N. Dange Computer Science Department, Walchand Institute of Rajesh V. Argiddi Assistant Prof. Computer Science Department,
ANALYTICS CENTER LEARNING PROGRAM
Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals
Chapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
Topics in basic DBMS course
Topics in basic DBMS course Database design Transaction processing Relational query languages (SQL), calculus, and algebra DBMS APIs Database tuning (physical database design) Basic query processing (ch
Efficient on-line Signature Verification System
International Journal of Engineering & Technology IJET-IJENS Vol:10 No:04 42 Efficient on-line Signature Verification System Dr. S.A Daramola 1 and Prof. T.S Ibiyemi 2 1 Department of Electrical and Information
SPATIAL DATA CLASSIFICATION AND DATA MINING
, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal
Role of Social Networking in Marketing using Data Mining
Role of Social Networking in Marketing using Data Mining Mrs. Saroj Junghare Astt. Professor, Department of Computer Science and Application St. Aloysius College, Jabalpur, Madhya Pradesh, India Abstract:
Data Mining System, Functionalities and Applications: A Radical Review
Data Mining System, Functionalities and Applications: A Radical Review Dr. Poonam Chaudhary System Programmer, Kurukshetra University, Kurukshetra Abstract: Data Mining is the process of locating potentially
An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015
An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content
How To Understand The History Of Navigation In French Marine Science
E-navigation, from sensors to ship behaviour analysis Laurent ETIENNE, Loïc SALMON French Naval Academy Research Institute Geographic Information Systems Group laurent.etienne@ecole-navale.fr loic.salmon@ecole-navale.fr
OUTLIER ANALYSIS. Data Mining 1
OUTLIER ANALYSIS Data Mining 1 What Are Outliers? Outlier: A data object that deviates significantly from the normal objects as if it were generated by a different mechanism Ex.: Unusual credit card purchase,
Distance Learning and Examining Systems
Lodz University of Technology Distance Learning and Examining Systems - Theory and Applications edited by Sławomir Wiak Konrad Szumigaj HUMAN CAPITAL - THE BEST INVESTMENT The project is part-financed
Knowledge Discovery from patents using KMX Text Analytics
Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers
Analysis of Social Media Streams
Fakultätsname 24 Fachrichtung 24 Institutsname 24, Professur 24 Analysis of Social Media Streams Florian Weidner Dresden, 21.01.2014 Outline 1.Introduction 2.Social Media Streams Clustering Summarization
Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management
Using reporting and data mining techniques to improve knowledge of subscribers; applications to customer profiling and fraud management Paper Jean-Louis Amat Abstract One of the main issues of operators
Data Isn't Everything
June 17, 2015 Innovate Forward Data Isn't Everything The Challenges of Big Data, Advanced Analytics, and Advance Computation Devices for Transportation Agencies. Using Data to Support Mission, Administration,
BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376
Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.
Classification of Household Devices by Electricity Usage Profiles
Classification of Household Devices by Electricity Usage Profiles Jason Lines 1, Anthony Bagnall 1, Patrick Caiger-Smith 2, and Simon Anderson 2 1 School of Computing Sciences University of East Anglia
A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization
A Partially Supervised Metric Multidimensional Scaling Algorithm for Textual Data Visualization Ángela Blanco Universidad Pontificia de Salamanca ablancogo@upsa.es Spain Manuel Martín-Merino Universidad
Quality Assessment in Spatial Clustering of Data Mining
Quality Assessment in Spatial Clustering of Data Mining Azimi, A. and M.R. Delavar Centre of Excellence in Geomatics Engineering and Disaster Management, Dept. of Surveying and Geomatics Engineering, Engineering
BIG DATA What it is and how to use?
BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14
IJCSES Vol.7 No.4 October 2013 pp.165-168 Serials Publications BEHAVIOR PERDITION VIA MINING SOCIAL DIMENSIONS
IJCSES Vol.7 No.4 October 2013 pp.165-168 Serials Publications BEHAVIOR PERDITION VIA MINING SOCIAL DIMENSIONS V.Sudhakar 1 and G. Draksha 2 Abstract:- Collective behavior refers to the behaviors of individuals
Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier
Data Mining: Concepts and Techniques Jiawei Han Micheline Kamber Simon Fräser University К MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF Elsevier Contents Foreword Preface xix vii Chapter I Introduction I I.
Machine Learning CS 6830. Lecture 01. Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu
Machine Learning CS 6830 Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu What is Learning? Merriam-Webster: learn = to acquire knowledge, understanding, or skill
2015 Workshops for Professors
SAS Education Grow with us Offered by the SAS Global Academic Program Supporting teaching, learning and research in higher education 2015 Workshops for Professors 1 Workshops for Professors As the market
A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS
A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS Mrs. Jyoti Nawade 1, Dr. Balaji D 2, Mr. Pravin Nawade 3 1 Lecturer, JSPM S Bhivrabai Sawant Polytechnic, Pune (India) 2 Assistant
Statistics Graduate Courses
Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.
Fluency With Information Technology CSE100/IMT100
Fluency With Information Technology CSE100/IMT100 ),7 Larry Snyder & Mel Oyler, Instructors Ariel Kemp, Isaac Kunen, Gerome Miklau & Sean Squires, Teaching Assistants University of Washington, Autumn 1999
Statistical Models in Data Mining
Statistical Models in Data Mining Sargur N. Srihari University at Buffalo The State University of New York Department of Computer Science and Engineering Department of Biostatistics 1 Srihari Flood of
Evaluating an Integrated Time-Series Data Mining Environment - A Case Study on a Chronic Hepatitis Data Mining -
Evaluating an Integrated Time-Series Data Mining Environment - A Case Study on a Chronic Hepatitis Data Mining - Hidenao Abe, Miho Ohsaki, Hideto Yokoi, and Takahira Yamaguchi Department of Medical Informatics,
Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization
Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing
REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc])
244 REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc]) (See also General Regulations) Any publication based on work approved for a higher degree should contain a reference
Index Contents Page No. Introduction . Data Mining & Knowledge Discovery
Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.
An Energy-Based Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network
Proceedings of the 8th WSEAS Int. Conf. on ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING & DATA BASES (AIKED '9) ISSN: 179-519 435 ISBN: 978-96-474-51-2 An Energy-Based Vehicle Tracking System using Principal
Applications of Deep Learning to the GEOINT mission. June 2015
Applications of Deep Learning to the GEOINT mission June 2015 Overview Motivation Deep Learning Recap GEOINT applications: Imagery exploitation OSINT exploitation Geospatial and activity based analytics
Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center
Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center 1 Outline Part I - Applications Motivation and Introduction Patient similarity application Part II
Machine learning for algo trading
Machine learning for algo trading An introduction for nonmathematicians Dr. Aly Kassam Overview High level introduction to machine learning A machine learning bestiary What has all this got to do with
College information system research based on data mining
2009 International Conference on Machine Learning and Computing IPCSIT vol.3 (2011) (2011) IACSIT Press, Singapore College information system research based on data mining An-yi Lan 1, Jie Li 2 1 Hebei
Diagnosis of Students Online Learning Portfolios
Diagnosis of Students Online Learning Portfolios Chien-Ming Chen 1, Chao-Yi Li 2, Te-Yi Chan 3, Bin-Shyan Jong 4, and Tsong-Wuu Lin 5 Abstract - Online learning is different from the instruction provided
Data Mining + Business Intelligence. Integration, Design and Implementation
Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution
Data Mining Applications in Higher Education
Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2
Galaxy Morphological Classification
Galaxy Morphological Classification Jordan Duprey and James Kolano Abstract To solve the issue of galaxy morphological classification according to a classification scheme modelled off of the Hubble Sequence,
FOREX TRADING PREDICTION USING LINEAR REGRESSION LINE, ARTIFICIAL NEURAL NETWORK AND DYNAMIC TIME WARPING ALGORITHMS
FOREX TRADING PREDICTION USING LINEAR REGRESSION LINE, ARTIFICIAL NEURAL NETWORK AND DYNAMIC TIME WARPING ALGORITHMS Leslie C.O. Tiong 1, David C.L. Ngo 2, and Yunli Lee 3 1 Sunway University, Malaysia,
Data Mining and Analysis of Online Social Networks
Data Mining and Analysis of Online Social Networks R.Sathya 1, A.Aruna devi 2, S.Divya 2 Assistant Professor 1, M.Tech I Year 2, Department of Information Technology, Ganadipathy Tulsi s Jain Engineering
COURSE RECOMMENDER SYSTEM IN E-LEARNING
International Journal of Computer Science and Communication Vol. 3, No. 1, January-June 2012, pp. 159-164 COURSE RECOMMENDER SYSTEM IN E-LEARNING Sunita B Aher 1, Lobo L.M.R.J. 2 1 M.E. (CSE)-II, Walchand
A Big Data Analytical Framework For Portfolio Optimization Abstract. Keywords. 1. Introduction
A Big Data Analytical Framework For Portfolio Optimization Dhanya Jothimani, Ravi Shankar and Surendra S. Yadav Department of Management Studies, Indian Institute of Technology Delhi {dhanya.jothimani,
Automated Process for Generating Digitised Maps through GPS Data Compression
Automated Process for Generating Digitised Maps through GPS Data Compression Stewart Worrall and Eduardo Nebot University of Sydney, Australia {s.worrall, e.nebot}@acfr.usyd.edu.au Abstract This paper
MS1b Statistical Data Mining
MS1b Statistical Data Mining Yee Whye Teh Department of Statistics Oxford http://www.stats.ox.ac.uk/~teh/datamining.html Outline Administrivia and Introduction Course Structure Syllabus Introduction to
Data Mining for Manufacturing: Preventive Maintenance, Failure Prediction, Quality Control
Data Mining for Manufacturing: Preventive Maintenance, Failure Prediction, Quality Control Andre BERGMANN Salzgitter Mannesmann Forschung GmbH; Duisburg, Germany Phone: +49 203 9993154, Fax: +49 203 9993234;
Biometric Authentication using Online Signatures
Biometric Authentication using Online Signatures Alisher Kholmatov and Berrin Yanikoglu alisher@su.sabanciuniv.edu, berrin@sabanciuniv.edu http://fens.sabanciuniv.edu Sabanci University, Tuzla, Istanbul,