Data Analytics and Business Intelligence (8696/8697)
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1 http: // togaware. com Copyright 2014, 1/39 Data Analytics and Business Intelligence (8696/8697) Introducing Data Mining Chief Data Scientist Australian Taxation Office Adjunct Professor, Australian National University Adjunct Professor, University of Canberra Fellow, Institute of Analytics Professionals of Australia
2 http: // togaware. com Copyright 2014, 2/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
3 Introductions http: // togaware. com Copyright 2014, 3/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
4 Introductions Beginning Data Mining http: // togaware. com Copyright 2014, 4/39 A Little History Database Research where to now? Machine Learning algorithms for symbolic learning. Statistics long heritage and role of sampling. Data Mining is one of the most important core technologies being developed today, in the same league as medical research, energy research, and environmental research. (David Hand, 2006) Data mining is a core technology underlying new developments in medicine, biotechnology, finance, environmental research.
5 Introductions Beginning Data Mining http: // togaware. com Copyright 2014, 5/39 Data Mining in Practise Data Mining research - CSIRO 1995 Data Mining in practise - Health Insurance Commission 1995 Data Mining projects with: Esanda Finance NRMA Mount Stromlo Health Insurance Commission Commonwealth Bank Department of Health Australian Taxation Office Australian Customs Service Department of Veteran Affairs...
6 Introductions Beginning Data Mining http: // togaware. com Copyright 2014, 5/39 Data Mining in Practise Data Mining research - CSIRO 1995 Data Mining in practise - Health Insurance Commission 1995 Data Mining projects with: Esanda Finance NRMA Mount Stromlo Health Insurance Commission Commonwealth Bank Department of Health Australian Taxation Office Australian Customs Service Department of Veteran Affairs...
7 Introductions Beginning Data Mining http: // togaware. com Copyright 2014, 5/39 Data Mining in Practise Data Mining research - CSIRO 1995 Data Mining in practise - Health Insurance Commission 1995 Data Mining projects with: Esanda Finance NRMA Mount Stromlo Health Insurance Commission Commonwealth Bank Department of Health Australian Taxation Office Australian Customs Service Department of Veteran Affairs...
8 Introductions Beginning Data Mining http: // togaware. com Copyright 2014, 5/39 Data Mining in Practise Data Mining research - CSIRO 1995 Data Mining in practise - Health Insurance Commission 1995 Data Mining projects with: Esanda Finance NRMA Mount Stromlo Health Insurance Commission Commonwealth Bank Department of Health Australian Taxation Office Australian Customs Service Department of Veteran Affairs...
9 Introductions Beginning Data Mining http: // togaware. com Copyright 2014, 6/39 Data Mining in Use Today Amazon What else should I buy ebay How to support sellers Facebook and LinkedIn Who should I be firends with? Google Who, what, when, how GoolgeNow Finance Bank products and fraud Health care Best treatment Insurance Fraud Government Better Services and Compliance
10 Setting the Scene http: // togaware. com Copyright 2014, 7/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
11 Setting the Scene http: // togaware. com Copyright 2014, 8/39 Data is Fundamental Sherlock Holmes: It is a capital mistake to theorize before one has data. Insensibly, one begins to twist facts to suit theories, instead of theories to suit facts. A Scandal in Bohemia (1891) Arthur Conan Doyle
12 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
13 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
14 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
15 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
16 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
17 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
18 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
19 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
20 Setting the Scene http: // togaware. com Copyright 2014, 9/39 The Economist, 21 September 2006 Combine dozens of clues to spot suspicious patterns in mountains of transactions. Car Insurance: the Monday morning fraudsters separating the genuine from the fraud Claimant nearly injured (driver side impact) low Low resale value high Low resale + own a luxury car low Low resale + own a luxury car expired insurance no Angry call after claim demanding action high Customer calls after the 20th of the month low But so many combinations!
21 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
22 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
23 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
24 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
25 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
26 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
27 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
28 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
29 Setting the Scene http: // togaware. com Copyright 2014, 10/39 Credit Card Fraud Association for Payment Clearing Services Buy petrol and pay by card using a machine hmmm... Soon after buy a diamond ring very high Purchase sports shoes marginally high Purchase two higher Teenage sizes high City has vibrant black market highest Professional fraudsters are innovative and dynamic we need to monitor and score individual cards and transactions. Block cards with sudden spikes in purchases.
30 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
31 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
32 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
33 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
34 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
35 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
36 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
37 Setting the Scene http: // togaware. com Copyright 2014, 11/39 Digital Footprints We leave behind us, every day, a growing digital footprint. Store Purchase - loyalty cards and credit cards Building Access Computer Login etoll Records Mobile Phone Cameras with sophisticated image recognition We need due diligence in collection and analysis of data privacy protocols.
38 Setting the Scene http: // togaware. com Copyright 2014, 12/39 Taxation Records - Historic Perspective Data Collection 5500 years ago Sumerian (Iraq) and Elam (Iran) peoples marked their tax records onto dried mud tablets. Data Analysis Since then people have sought ways to add value to this otherwise statically recorded information either for good or bad! Source
39 Setting the Scene http: // togaware. com Copyright 2014, 12/39 Taxation Records - Historic Perspective Data Collection 5500 years ago Sumerian (Iraq) and Elam (Iran) peoples marked their tax records onto dried mud tablets. Data Analysis Since then people have sought ways to add value to this otherwise statically recorded information either for good or bad! Source
40 Setting the Scene http: // togaware. com Copyright 2014, 12/39 Taxation Records - Historic Perspective Data Collection 5500 years ago Sumerian (Iraq) and Elam (Iran) peoples marked their tax records onto dried mud tablets. Data Analysis Since then people have sought ways to add value to this otherwise statically recorded information either for good or bad! Source
41 Terminology http: // togaware. com Copyright 2014, 13/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
42 Terminology http: // togaware. com Copyright 2014, 14/39 Machine Learning as Model Building Data Mining is a data driven approach to understanding the world. Deploys machine learning and statistical modelling approaches to build models from large data collections. We are aiming to build models of the real world, as an architect would build models to get a better understanding of how things will look.
43 Terminology http: // togaware. com Copyright 2014, 14/39 Machine Learning as Model Building Data Mining is a data driven approach to understanding the world. Deploys machine learning and statistical modelling approaches to build models from large data collections. We are aiming to build models of the real world, as an architect would build models to get a better understanding of how things will look.
44 Terminology http: // togaware. com Copyright 2014, 14/39 Machine Learning as Model Building Data Mining is a data driven approach to understanding the world. Deploys machine learning and statistical modelling approaches to build models from large data collections. We are aiming to build models of the real world, as an architect would build models to get a better understanding of how things will look.
45 Terminology http: // togaware. com Copyright 2014, 15/39 Data Mining as Model Building Build models of the world (regression, decision trees, neural networks, association rules, fuzzy systems, random forests, support vector machines, boosted stumps,... ) from data that represent snippets of information about the world. Use these models to understand and discover patterns of interest that may provide knowledge deployable in improving business processes. The non-trivial extraction of novel, implicit, and actionable knowledge from large databases and in a timely manner. Knowledge from data any way you can. Technology to enable data exploration, data analysis, and data visualisation of very large databases at a high level of abstraction, without a specific hypothesis in mind.
46 Terminology http: // togaware. com Copyright 2014, 15/39 Data Mining as Model Building Build models of the world (regression, decision trees, neural networks, association rules, fuzzy systems, random forests, support vector machines, boosted stumps,... ) from data that represent snippets of information about the world. Use these models to understand and discover patterns of interest that may provide knowledge deployable in improving business processes. The non-trivial extraction of novel, implicit, and actionable knowledge from large databases and in a timely manner. Knowledge from data any way you can. Technology to enable data exploration, data analysis, and data visualisation of very large databases at a high level of abstraction, without a specific hypothesis in mind.
47 Terminology http: // togaware. com Copyright 2014, 15/39 Data Mining as Model Building Build models of the world (regression, decision trees, neural networks, association rules, fuzzy systems, random forests, support vector machines, boosted stumps,... ) from data that represent snippets of information about the world. Use these models to understand and discover patterns of interest that may provide knowledge deployable in improving business processes. The non-trivial extraction of novel, implicit, and actionable knowledge from large databases and in a timely manner. Knowledge from data any way you can. Technology to enable data exploration, data analysis, and data visualisation of very large databases at a high level of abstraction, without a specific hypothesis in mind.
48 Terminology http: // togaware. com Copyright 2014, 15/39 Data Mining as Model Building Build models of the world (regression, decision trees, neural networks, association rules, fuzzy systems, random forests, support vector machines, boosted stumps,... ) from data that represent snippets of information about the world. Use these models to understand and discover patterns of interest that may provide knowledge deployable in improving business processes. The non-trivial extraction of novel, implicit, and actionable knowledge from large databases and in a timely manner. Knowledge from data any way you can. Technology to enable data exploration, data analysis, and data visualisation of very large databases at a high level of abstraction, without a specific hypothesis in mind.
49 Terminology http: // togaware. com Copyright 2014, 16/39 Data Mining Adds Value to Data Competitive business environment knowledge is power Improved knowledge for better decision support Volumes of data unexplored wealth of information Data sources accessible massive data warehouses Available data mining tools and computational power
50 Applications http: // togaware. com Copyright 2014, 17/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
51 Applications http: // togaware. com Copyright 2014, 18/39 Business Problems Customer Profiling Customer Segmentation Direct Marketing Customer Retention Basket Analysis Fraud Detection Compliance Adverse Drug Reactions...
52 Applications http: // togaware. com Copyright 2014, 19/39 Customer Relationship Management All-of-business view of customer E-commerce providing significant data for mining Amazon.com a data mining company! ATO (and other Government Departments) CRM through data mining
53 Applications http: // togaware. com Copyright 2014, 20/39 Fraud Disguised amongst the mass of data A small percentage of all transactions A small percentage of a very large budget 0.1% of $1billion = $1million NRMA: Fraud costs $70 per policy Dob in a fraud: Canberra Times 16 Aug 2003 ATO and serious non-compliance
54 Applications http: // togaware. com Copyright 2014, 21/39 Characteristics Extremely large databases Discovery of the non-obvious No specific hypothesis c.f. statistical hypothesis testing Useful knowledge to improve processes Too large to be done manually
55 Technologies http: // togaware. com Copyright 2014, 22/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
56 Technologies http: // togaware. com Copyright 2014, 23/39 Technologies Machine Learning Database Web Services High Performance Computers Data Mining Statistics Computational Mathematics Intelligent Systems
57 Technologies http: // togaware. com Copyright 2014, 24/39 A Framework for Data Mining A Formal Framework with Three Elements: Knowledge Representation - Language and Sentences Measure of Goodness Heuristic Search - near infinite search space
58 Technologies http: // togaware. com Copyright 2014, 25/39 Machine Learning Representing Knowledge Languages: Decision Trees Classification Rules Neural Networks Support Vector Machine K Nearest Neighbours Cluster Centroids Association Rules Heuristic search for the best sentences (models) in the language by some measure (criteria of best model) Age... Gender Distance Income... GP Visits $ Risk Age Hospital? Support Vectors
59 Technologies Statistics Supporting sampling and uncertainty and modelling: Data, Counting, Probabilities, Hypothesis testing Exploratory data analysis Predictive models: CART, MARS, PRIM Statistical thinking and avoiding data dredging Computational requirements sampling Data mining attempts to avoid sampling Data mining: hypothesis generation c.f. hypothesis testing Be aware of the Bonferroni correction: testing 2 hypotheses, then use a p-value threshold of http: // togaware. com Copyright 2014, Graham.Williams@togaware.com 26/39
60 Technologies http: // togaware. com Copyright 2014, 27/39 Visualisation Data visualisation is key to understanding our data, its distribution, and models Exploratory data analysis has long history in Statistics, now significantly enhanced by computer science Visualisation in Data Mining Understanding the data Visualising the process Visualising the results of mining See Minard s visualisation of Napolean s march on Moscow.
61 Technologies http: // togaware. com Copyright 2014, 28/39 High Performance Computing Most algorithms are computationally expensive Very large datasets versus slow algorithms Parallel computers Multiprocessor computers Moving back to 64bit processors 32bit is limited to 4GB of data 64bit is limited to 16 EB (exabytes) (16,000 PB = 16,000,000 TB = 16,000,000,000 GB)
62 Technologies http: // togaware. com Copyright 2014, 29/39 Software Engineering SE Practices apply to DM practises Model development process Repeatability and Evidence Developing APIs for data and models SOAP Delivery of data mining through web services Interoperability through PMML (XML) Predictive Modelling Markup Language
63 Techniques http: // togaware. com Copyright 2014, 30/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
64 Techniques http: // togaware. com Copyright 2014, 31/39 Techniques Data mining can be descriptive or predictive. Descriptive: Identify human-interpretable patterns. Visualisation Concept Description Cluster Analysis Association Analysis Sequential Patterns Anomaly Detection Predictive: Predict unknown or future outcomes. Regression predict a continuous quantity Classification predict class of entities
65 Techniques http: // togaware. com Copyright 2014, 32/39 Concept Description Characterise and discriminate between different concepts (classes or groups) of entities (customers) Example: Amazon.com interested in the characteristics of people who tend to purchase many books so that they can provide a better service for them. Characterisation identifies general characteristics or features of a target class of data. Discrimination compares features of different classes for those that differentiate.
66 Techniques http: // togaware. com Copyright 2014, 33/39 Cluster Analysis Identify groups within data that maximise intraclass similarity and minimises interclass similarity. Example: Cluster all customers based on personal characteristics and products they ve purchased. Building models from unlabelled data: unsupervised learning.
67 Techniques http: // togaware. com Copyright 2014, 34/39 Association Analysis Identify attribute-value conditions that occur frequently together in a given set of data. Example: Age [20, 30] Income [$20K, $30K] MP3player Support=2%; Confidence=60%
68 Techniques http: // togaware. com Copyright 2014, 35/39 Anomaly Detection Also referred to as Outlier Analysis Identify entities that do not comply with the general behaviour or model of data. Exceptions to the rule! Example: Odd patterns can be easily hidden amongst 10 million transactions, but may be indicative of fraud.... but not likely find those who live at the edge
69 Techniques http: // togaware. com Copyright 2014, 36/39 Classification Find a model which describes and distinguishes data classes or concepts for the purpose of using the model to predict the class of previously unseen entities. Example: Build a model to predict whether customers are likely to purchase a download of a particular MP3 music file. Building models from labelled data: supervised learning.
70 Issues http: // togaware. com Copyright 2014, 37/39 Overview 1 Introductions 2 Setting the Scene 3 Data Mining Terminology 4 Data Mining Applications 5 Data Mining Technologies 6 Data Mining Techniques 7 Issues
71 Issues http: // togaware. com Copyright 2014, 38/39 Data Challenges 1 Scalability 2 Dimensionality 3 Complex and Heterogeneous Data 4 Data Quality 5 Data Matching and Linking 6 Privacy 7 Streaming: Anytime Data Mining give me the answer now
72 Summary Introducing Data Mining http: // togaware. com Copyright 2014, 39/39 Lecture Summary Data Mining is about Analysing Data; Technology that is all pervasive today; Draws on many disciplines; Key disciplines of Statistics and Machine Learning. This document, sourced from DataMiningL.Rnw revision 436, was processed by KnitR version 1.6 of and took 1.1 seconds to process. It was generated by gjw on nyx running Ubuntu LTS with Intel(R) Xeon(R) CPU 2.67GHz having 4 cores and 12.3GB of RAM. It completed the processing :22:53.
How To Understand Data Mining In R And Rattle
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