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Tutorial on Data Mining Workshop of the Indian Database Research Community Sunita Sarawagi School of IT, IIT Bombay Data mining • Process of semi-automatically analyzing large databases to find interesting and useful patterns • Overlaps with machine learning, statistics, artificial intelligence and databases but – more scalable in number of features and instances – more automated to handle heterogeneous data Outline • • • • • • Applications Usage scenarios Overview of operations Mining research groups Relevance in India Ten research problems Applications • Customer relationship management: – identify those who are likely to leave for a competitor. • • • • • • • Targeted marketing: identify likely responders to promotions Fraud detection: telecommunications, financial transactions Manufacturing and production: Medicine: disease outcome, effectiveness of treatments Molecular/Pharmaceutical: identify new drugs Scientific data analysis: Web site/store design and promotion Usage scenarios • Data warehouse mining: – assimilate data from operational sources – mine static data • Mining log data • Continuous mining: example in process control • Stages in mining: – data selection pre-processing: cleaning transformation mining result evaluation visualization Some basic operations • Predictive: – Regression – Classification • Descriptive: – Clustering / similarity matching – Association rules and variants – Deviation detection Classification • Given old data about customers and payments, predict new applicant’s loan eligibility. Previous customers Age Salary Profession Location Customer type Classifier Decision rules Salary > 5 L Prof. = Exec New applicant’s data Good/ bad Classification methods Goal: Predict class Ci = f(x1, x2, .. Xn) • Regression: (linear or any other polynomial) – a*x1 + b*x2 + c = Ci. • Nearest neighour • Decision tree classifier: divide decision space into piecewise constant regions. • Probabilistic/generative models • Neural networks: partition by non-linear boundaries Nearest neighbor • Define proximity between instances, find neighbors of new instance and assign majority class • Case based reasoning: when attributes are more complicated than real-valued. • Pros + Fast training • Cons – Slow during application. – No feature selection. – Notion of proximity vague Decision trees • Tree where internal nodes are simple decision rules on one or more attributes and leaf nodes are predicted class labels. Salary < 1 M Prof = teacher Good Bad Age < 30 Bad Good Algorithm for tree building • Greedy top-down construction. Gen_Tree (Node, data) make node a leaf? Yes Stop Selection Find best attribute and best split on attribute criteria Partition data on split condition For each child j of node Gen_Tree (node_j, data_j) Split criteria • K classes, set of S instances partitioned into r subsets. Instance Sj has fraction pij instances of class j. 1/4 • Information entropy: r Sj k Gini pij log pij j 1 S i 1 • Gini index: r Sj 0 k S (1 p ) j 1 i 1 2 ij 1 Impurity r =1, k=2 Scalable algorithm • Input: table of records • Vertically partition data and sort on <attribute value, class> • Finding best split: – Scan and maintain class counts in memory and find gini A1 C rid incrementally. • Performing split: – Use split attribute to build rid to L/R hash in memory. – Divide other attributes using above hash table. rid A1 A2 A3 C A2 C rid A3 C rid Issues • Preventing overfitting – Occam’s razor: • prefer the simplest hypothesis that fits the data – Tree pruning methods: • Cross validation with separate test data • Minimum description length (MDL) criteria • Multi attribute tests on nodes to handle correlated attributes – Linear multivariate – Non-linear multivariate e.g. a neural net at each node. • Methods of handling missing values Pros and Cons of decision trees • Pros + Reasonable training time + Fast application + Easy to interpret + Easy to implement + Can handle large number of features • Cons – Cannot handle complicated relationship between features – simple decision boundaries – problems with lots of missing data More information: http://www.stat.wisc.edu/~limt/treeprogs.html Neural networks • Useful for learning complex data like handwriting, speech and image recognition Decision boundaries: Classification tree Neural network Neural network • Set of nodes connected by directed weighted edges Basic NN unit x1 w1 x2 w2 x3 w3 A more typical NN n x1 i 1 x2 o ( wi xi ) 1 ( y) 1 e y x3 Output nodes Hidden nodes Pros and Cons of Neural Network • Pros + Can learn more complicated class boundaries + Fast application + Can handle large number of features • Cons – Slow training time – Hard to interpret – Hard to implement: trial and error for choosing number of nodes Conclusion: Use neural nets only if decision trees/NN fail. Bayesian learning • Assume a probability model on generation of data. • Apply bayes theorem to find most likely class as: p(d | c j ) p(c j ) predicted class : c max p(c j | d ) max cj cj p(d ) • Naïve bayes: Assume attributes conditionally independent given class value p(c j ) n c max p( ai | c j ) cj p( d ) i 1 • Easy to learn probabilities by counting, • Useful in some domains e.g. text Bayesian belief network • Find joint probability over set of variables making use of conditional independence whenever known a d ad ad ad ad b Variable e independent of d given b e b 0.1 0.2 0.3 0.4 b 0.3 0.2 0.1 0.5 C • Learning parameters hard when hidden units: use gradient descent / EM algorithms • Learning structure of network harder Clustering • Unsupervised learning when old data with class labels not available e.g. when introducing a new product to a customer base • Group/cluster existing customers based on time series of payment history such that similar customers in same cluster. • Identify micro-markets and develop policies for each • Key requirement: Need a good measure of similarity between instances Distance functions • Numeric data: euclidean, manhattan distances • Categorical data: 0/1 to indicate presence/absence followed by – Hamming distance (# dissimilarity) – Jaccard coefficients: #similarity in 1s/(# of 1s) – data dependent measures: similarity of A and B depends on co-occurance with C. • Combined numeric and categorical data: – weighted normalized distance: Distance functions on high dimensional data • Example: Time series, Text, Images • Euclidian measures make all points equally far • Reduce number of dimensions: – choose subset of original features using random projections, feature selection techniques – transform original features using statistical methods like Principal Component Analysis • Define domain specific similarity measures: e.g. for images define features like number of objects, color histogram; for time series define shape based measures. • Define non-distance based (model-based) clustering methods: Clustering methods • Hierarchical clustering – agglomerative Vs divisive – single link Vs complete link • Partitional clustering – distance-based: K-means – model-based: EM – density-based: Partitional methods: K-means • Criteria: minimize sum of square of distance • Between each point and centroid of the cluster. • Between each pair of points in the cluster • Algorithm: – Select initial partition with K clusters: random, first K, K separated points – Repeat until stabilization: • Assign each point to closest cluster center • Generate new cluster centers • Adjust clusters by merging/splitting Properties • May not reach global optima • Converges fast in practice: guaranteed for certain forms of optimization function • Complexity: O(KndI): – I number of iterations, n number of points, d number of dimensions, K number of clusters. • Database research on scalable algorithms: – Birch: one/two pass of data by keeping R-tree like index in memory [Sigmod 96] – Model based clustering • Assume data generated from K probability distributions. Need to find distribution parameters. EM algorithm: K Gaussian mixtures • Iterate between two steps – Expectation step: assign points to clusters P( d i ck ) Pr( N ( k , 2 ), d i ) – Maximation step: estimate model parameters k d P(d c ) P(d c ) i i k i i i k Association rules • Given set T of groups of items • Example: set of baskets of items purchased • Goal: find all rules on itemsets of the form a-->b such that T Milk, cereal Tea, milk Tea, rice, bread – support of a and b > user threshold s – conditional probability (confidence) of b given a > user threshold c • Example: Milk --> bread • Lot of work done on scalable algorithms cereal Variants • High confidence may not imply high correlation • Use correlations. Find expected support and large departures from that interesting. – Brin et al. Limited attempt. – More complete work in statistical literature on contingency tables. • Still too many rules, need to prune... • Does not imply causality as in Bayesian networks Prevalent Interesting • Analysts already know about prevalent rules • Interesting rules are those that deviate from prior expectation • Mining’s payoff is in finding surprising phenomena Zzzz... 1995 Milk and cereal sell together! 1998 Milk and cereal sell together! What makes a rule surprising? • Does not match prior expectation – Correlation between milk and cereal remains roughly constant over time • Cannot be trivially derived from simpler rules – Milk 10%, cereal 10% – Milk and cereal 10% … surprising – Eggs 10% – Milk, cereal and eggs 0.1% … surprising! – Expected 1% Applications of fast itemset counting Find correlated events: • Applications in medicine: find redundant tests • Cross selling in retail, banking • Improve predictive capability of classifiers that assume attribute independence • New similarity measures of categorical attributes [Mannila et al, KDD 98] Mining market • Around 20 to 30 mining tool vendors: 1/5th the size of OLAP market. • Major players: – – – – Clementine, IBM’s Intelligent Miner, SGI’s MineSet, SAS’s Enterprise Miner. • All pretty much the same set of tools • Many embedded products: fraud detection, electronic commerce applications Integrating mining with DBMS • Need to – intermix operations – iterate through results – flexibly query and filter results and data • Existing file-based, batched approach not satisfactory. • Research challenge: Identify a collection of primitive, composable operators like in relational DBMS and build a “mining engine” OLAP Mining integration • OLAP (On Line Analytical Processing) – Multidimensional view of data: factors are dimensions, quantity to be analyszed: measures/cells. – Facilitates fast interactive exploration of multidimensional aggregates. • OLAP products provide a minimal set of tools for analysis: • Heavy reliance on manual operations for analysis: – tedious and error-prone on large multidimensional data • Ideal platform for vertical integration of mining but needs to be interactive instead of batch. State of art in mining OLAP integration • Decision trees [Information discovery, Cognos] – find factors influencing high profits • Clustering [Pilot software] – segment customers to define hierarchy on that dimension • Time series analysis: [Seagate’s Holos] – Query for various shapes along time: spikes, outliers etc • Multi-level Associations [Han et al.] – find association between members of dimensions New approach Identify complex operations with specific OLAP needs in mind (what does an analyst need?) rather than looking at mining operations and choosing what fits Two examples: • Exceptions in data to guide exploration: – One reason for manual exploration is to make sure that there are no surprises. – Pre-mines abnormalities in data and points them out to analysts using highlights at aggregate levels • Reasons for specific why questions at aggregate level – most compactly represent the answer that user can quickly assimilate Vertical integration: Mining on the web • Web log analysis for site design: – what are popular pages, – what links are hard to find. • Electronic stores sales enhancements: – recommendations, advertisement: – Collaborative filtering: Net perception, Wisewire – Inventory control: what was a shopper looking for and could not find.. Research problems • Automatic model selection: different ways of solving same problem, which one to use? • Automatic classification of complex data types especially time series data. • Refreshing mined results: explaining and modeling changes along time • Quality of mined results: guarding against wrong conclusions, chance discovering • Incorporating domain knowledge to filter results and improve result quality Research problems • Close integration with data sources to be mined • Distributed mining across multiple relations at a single site or spread across multiple sites. • Integration with other data analysis tools: example statistical tools, OLAP and SQL querying • Interactive data mining: toolkit of micro operators • Mixed media mining: link textual reports with images and numeric fields Relevance in India • Emerging application areas especially in the banking, retail industry and manufacturing processes • Mining large scientific databases: export laws might require indigeneous technology • Rich research area with interesting algorithm components -- just need to implement. • Too expensive to purchase US/Europe products Need to build usable prototypes not simply tweak algorithms for publications. Summary • What is data mining and an overview of the various operations: – Classification: regression, nearest neighbour, neural network, bayesian – Clustering: distance based (k-means), distribution based(EM) – Itemset counting • Several operations: challenge is choosing the right operation for the problem • New directions and identification of research problems