Databases: Visualization, Data Mining, New DB Paradigms. Thomas Weik FH Münster

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1 Databases: Visualization, Data Mining, New DB Paradigms Thomas Weik FH Münster

2 9. Basic Mining Strategies 9.0 References 9.1 Motivation 9.2 Classification 9.3 Clustering 9.4 Association Rule Discovery 9.5 Challenges of Data Mining Thomas Weik: DWH and Data Mining WS 2014 /

3 9.0 References: Books Books: Witten, Eibe, Hall: Data Mining Practical Machine Learning Tools and Techniques; 3rd Edition, Morgan Kaufman 2011 Han et al.: Data Mining Concepts and Techniques, Morgan Kaufman 2011 North: Data Mining for the Masses: DataMiningForTheMasses.pdf Thomas Weik: DWH and Data Mining WS 2014 /

4 9.0 References: Software Software: WEKA: Rapid Miner: Manual: manual-english_v1.0.pdf KNIME (Konstanz Information Miner): R: CLI for Statistical Computing, Graphics and Data Mining: Thomas Weik: DWH and Data Mining WS 2014 /

5 9.1 Why Mine Data? There is often information hidden in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all Thomas Weik: DWH and Data Mining WS 2014 /

6 9.1 Orders of Magnitude 1 PB is enough to store the DNA of every person in the US with cloning it twice... AT&T transfers 30 PB of data through its network per day. Until July 2012 CERN amassed about 200 PB of data about 800 trillion collisions in search for the Higgs boson. 1 PB of MP3 encoded music plays continously for about 2000 years. IDC: Total amount of global data was expected to grow to 2.7 ZB in 2012, which is an increase of 48% from Whistleblower: NSA's Utah Data Center will have a capacity of about 5 ZB when completed. Thomas Weik: DWH and Data Mining WS 2014 /

7 9.1 Orders of Magnitude According to an IDC paper sponsored by EMC Corporation, 161 exabytes of data were created in 2006, "3 million times the amount of information contained in all the books ever written", with the number expected to hit 988 exabytes in (Wikipedia.org) Thomas Weik: DWH and Data Mining WS 2014 /

8 9.1 Why Mine Data? Commercial Viewpoint Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong Provide better, customized services for an edge (e.g. in Customer Relationship Management) Thomas Weik: DWH and Data Mining WS 2014 /

9 9.1 Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite telescopes scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists in classifying and segmenting data in Hypothesis Formation Thomas Weik: DWH and Data Mining WS 2014 /

10 9.1 What is (not) Data Mining? What is not Data Mining? Look up phone number in phone directory Query a Web search engine for information about Amazon What is Data Mining? Certain names are more prevalent in certain US locations (O Brien, O Rurke, O Reilly in Boston area) Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) Thomas Weik: DWH and Data Mining WS 2014 /

11 9.1 Origins of Data Mining Draws ideas from machine learning/ai, pattern recognition, statistics, and database systems Traditional Techniques may be unsuitable due to Enormity of data High dimensionality of data Heterogeneous, distributed nature of data Statistics/ AI Data Mining Database systems Machine Learning/ Pattern Recognition Thomas Weik: DWH and Data Mining WS 2014 /

12 9.1 What is Data Mining? Many Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns Data Mining needs a process! Thomas Weik: DWH and Data Mining WS 2014 /

13 9.2 Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it. Thomas Weik: DWH and Data Mining WS 2014 /

14 Illustrating Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes Training Set Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K? 12 Yes Medium 80K? 13 Yes Large 110K? 14 No Small 95K? 15 No Large 67K? Test Set Induction Deduction Learning algorithm Learn Model Apply Model Model Thomas Weik: DWH and Data Mining WS 2014 /

15 Example of a Decision Tree Tid Refund Marital Status Taxable Income Cheat Splitting Attributes 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes Refund Yes No NO MarSt Single, Divorced TaxInc < 80K > 80K NO YES Married NO Training Data Model: Decision Tree Thomas Weik: DWH and Data Mining WS 2014 /

16 9.2 Classification Techniques Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines Thomas Weik: DWH and Data Mining WS 2014 /

17 9.2 Ex. for Classification Sky Survey Cataloging Goal: To predict class (star or galaxy) of sky objects, especially visually faint ones, based on the telescopic survey images (from Palomar Observatory) images with 23,040 x 23,040 pixels per image. Approach: Segment the image. Measure image attributes (features) - 40 of them per object. Model the class based on these features. Success Story: Could find 16 new high red-shift quasars, some of the farthest objects that are difficult to find! From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996 Thomas Weik: DWH and Data Mining WS 2014 /

18 9.2 Classifying Galaxies Early Class: Stages of Formation Intermediate Attributes: Image features, Characteristics of light waves received, etc. Late Data Size: 72 million stars, 20 million galaxies Object Catalog: 9 GB Image Database: 150 GB Courtesy: Thomas Weik: Data Mining WS 2014 /

19 9.2 Examples of Classification Predicting tumor cells as benign or malignant Classifying credit card transactions as legitimate or fraudulent Classifying secondary structures of protein as alpha-helix, beta-sheet, or random coil Categorizing news stories as finance, weather, entertainment, sports, etc Gene defect analysis Customer Rating Thomas Weik: DWH and Data Mining WS 2014 /

20 9.2 Constructing Decision Trees: Another Example Thomas Weik: DWH and Data Mining WS 2014 /

21 9.2 Constructing Decision Trees: Generic Algorithm Generic recursive algorithm: Select an attribute to place at the root node Make one branch for every possible value Thus the example set is split up into subsets One for every value of the attribute Repeat this process recursively for each branch Use only instances that actually reach this branch If all instances at a node have the same class value, then stop developing that part of the tree Thomas Weik: DWH and Data Mining WS 2014 /

22 9.2 Constructing Decision Trees: Problem Which attribute should we split on?? Thomas Weik: DWH and Data Mining WS 2014 /

23 9.2 Constructing Decision Trees: Information I Any leaf with only one class will not have to be split further Of course we seek small trees Solution: Measure for Purity of a node Choose attribute which produces the purest daughter nodes Measure of Purity: Information (unit: bits) For each node: expected amount of information to classify a new instance ( yes or no ) Thomas Weik: DWH and Data Mining WS 2014 /

24 9.2 Constructing Decision Trees: Information II Calculation based on # of yes and no classes at node # of yes and no at the leaf nodes?? Required properties of Information: When # of yes or no is zero, Information should be 0 When # of yes and no is equal, Information reaches a max value Measure should be applicable in multiclass situations Thomas Weik: DWH and Data Mining WS 2014 /

25 9.2 Expanded Tree Stumps Thomas Weik: DWH and Data Mining WS 2014 /

26 9.2 Resulting Decision Tree Thomas Weik: DWH and Data Mining WS 2014 /

27 9.2 The Measure: Entropy Only one function satisfies all these properties: Information value or entropy entropy(p 1,, p n ) = -p 1 log p p n log p n Thus: Info ([2,3]) = -2/5 x log 2/5 3/5 x log 3/5 = bits Thomas Weik: DWH and Data Mining WS 2014 /

28 This algorithm: ID3 Very robust 9.2 Improvements Later variant: C4.5 Also numeric attributes Missing values Noisy data Generating rules from trees Commercial version: C5.0 Some differences Negligible improvements over C4.5 We used J48: Implements C4.5 Rev. 8 Thomas Weik: DWH and Data Mining WS 2014 /

29 9.3 Clustering: Application 1 Market Segmentation: Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. Approach: Collect different attributes of customers based on their geographical and lifestyle related information. Find clusters of similar customers. Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters. Thomas Weik: Data Mining WS 2014 /

30 9.3 Clustering: Application 2 Document Clustering: Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents. Thomas Weik: Data Mining WS 2014 /

31 9.3 Illustrating Document Clustering Clustering Points: 3204 Articles of Los Angeles Times. Similarity Measure: How many words are common in these documents (after some word filtering). Category Total Correctly Articles Placed Financial Foreign National Metro Sports Entertainment Thomas Weik: Data Mining WS 2014 /

32 9.4 Association Rule Discovery: Definition Given a set of records each of which contain some number of items from a given collection; Produce dependency rules which will predict occurrence of an item based on occurrences of other items. TID Items 1 Bread, Coke, Milk 2 Beer, Bread 3 Beer, Coke, Diaper, Milk 4 Beer, Bread, Diaper, Milk 5 Coke, Diaper, Milk Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} Thomas Weik: Data Mining WS 2014 /

33 9.4 Association Rule Discovery: An Application Marketing and Sales Promotion: Let the rule discovered be {Bagels, } --> {Potato Chips} Potato Chips as consequent => Can be used to determine what should be done to boost its sales. Bagels in the antecedent => Can be used to see which products would be affected if the store discontinues selling bagels. Bagels in antecedent and Potato chips in consequent => Can be used to see what products should be sold with Bagels to promote sale of Potato chips! Thomas Weik: Data Mining WS 2014 /

34 9.4 Association Rule Discovery: An Application II Supermarket shelf management. Goal: To identify items that are bought together by sufficiently many customers. Approach: Process the point-of-sale data collected with barcode scanners to find dependencies among items. A classic rule -- If a customer buys diapers and milk, then he is very likely to buy beer. So, don t be surprised if you find six-packs stacked next to diapers! Thomas Weik: Data Mining WS 2014 /

35 9.4 Constructing a Data Warehouse for Breast Cancer Screening One of the most widespread kinds of cancer 55,000 new cases / year in Germany (80,000,000 inhabitants) Decreasing mortality, yet 18,000 deaths / year Mammography-Screening state-sponsored program for early detection through x-ray of breasts Introduction of one Screening Unit per 1,000,000 inhabitants Invitation of women by centralised unit Reference Center for North-Rhine Westphalia: University Clinique in Münster Thomas Weik: Data Mining WS 2014 /

36 9.4 Goals of Screening The earlier detected, the better the chances of cure Small rate of false positives and false negatives Decrease fear of x-ray (e.g. Tchernobyl) Decrease of mortality Rating of analogous and digital screening systems Analysis of screening participation trends and patterns, applied x-ray doses diagnoses Thomas Weik: Data Mining WS 2014 /

37 9.4 Data Sources Data of Central Invitation Units No. of invitations, no. of acceptance, data about screening units Data of medical doctors No. of invitations / acceptance Detected cases tumors Results of biopsies (malign tumors, non-malign tumors, false positives) Distribution of tumor stages Data about x-ray exposition of used screening equipment Regional distribution etc. Data about used screening technologies Data about acceptance tests of used systems Thomas Weik: Data Mining WS 2014 /

38 9.4 Extraction, Transform. and Analysis Legal Problems Standard transformation problems Large number of pre-defined reports Opportunity for ad-hoc reports and queries Data Mining and additional data sources will be added in further project Conclusions: Functioning system created in limited time Realisation of legal requirements and much more Good extensibility Thomas Weik: Data Mining WS 2014 /

39 9.5 Challenges of Data Scalability Mining Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data Thomas Weik: DWH and Data Mining WS 2014 /

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