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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.
AgeSalaryProfessionLocationCustomer type
Previous customers 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
• Define proximity between instances, find neighbors of new instance and assign majority class
• Case based reasoning: when attributes are more complicated than real-valued.
Nearest neighbor
• Cons– Slow during application.– No feature selection.– Notion of proximity vague
• Pros+ Fast training
• Tree where internal nodes are simple decision rules on one or more attributes and leaf nodes are predicted class labels.
Decision trees
Salary < 1 M
Prof = teacher
Good
Age < 30
BadBad Good
Algorithm for tree building• Greedy top-down construction.
Gen_Tree (Node, data)
make node a leaf? Yes Stop
Find best attribute and best split on attribute
Partition data on split condition
For each child j of node Gen_Tree (node_j, data_j)
Selectioncriteria
Split criteria• K classes, set of S instances partitioned into r
subsets. Instance Sj has fraction pij instances of class j.
• Information entropy:
• Gini index:
r
j
k
iijij
j ppS
S
1 1
log
)(1 1
21
r
j
k
iij
j pS
S0 1
Impurity
1/4
Gini
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 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
A1 C rid 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
• Cons– Cannot handle complicated relationship between features– simple decision boundaries– problems with lots of missing data
• Pros+ Reasonable training time+ Fast application+ Easy to interpret+ Easy to implement+ Can handle large number of features
More information: http://www.stat.wisc.edu/~limt/treeprogs.html
Neural networks• Useful for learning complex data like handwriting,
speech and image recognition
Neural networkClassification tree
Decision boundaries:
Neural network• Set of nodes connected by directed weighted
edges
Hidden nodes
Output nodes
x1
x2
x3
x1
x2
x3
w1
w2
w3
y
n
iii
ey
xwo
1
1)(
)(1
Basic NN unit A more typical NN
Pros and Cons of Neural Network
• Cons– Slow training time– Hard to interpret – Hard to implement: trial and error for choosing number of nodes
• Pros+ Can learn more complicated class boundaries+ Fast application+ Can handle large number of features
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:
• Naïve bayes: Assume attributes conditionally independent given class value
• Easy to learn probabilities by counting, • Useful in some domains e.g. text
)(
)()|(max)|(max :class predicted
dp
cpcdpdcpc jj
cj
c jj
n
iji
j
ccap
dp
cpc
j 1
)|()(
)(max
Bayesian belief network• Find joint probability over set of variables making
use of conditional independence whenever known
• Learning parameters hard when hidden units: use gradient descent / EM algorithms
• Learning structure of network harder
b bb
a d
eC
ad ad ad ad
0.1 0.2 0.3 0.4
0.3 0.2 0.1 0.5Variable e independent
of d given b
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
– Maximation step: estimate model parameters
)),,(Pr() ( 2ikki dNcdP
iki
ikii
k cdP
cdPd
) (
) (
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– 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
Milk, cerealTea, milk
Tea, rice, bread
cereal
T
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
1995
1998
Milk andcereal selltogether!
Zzzz... Milk andcereal selltogether!
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 countingFind 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
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