Data Mining: Concepts and Techniques (3rd ed.) Chapter

Data Mining: Concepts and Techniques (3rd ed.) Chapter 11 Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2009 Han, Kamber & Pei. All rights reserved. 02/09/20 1 February 9, 2020 Data Mining: Concepts and Techniques 2 Chapter 11. Cluster Analysis: Advanced Methods Statistics-Based Clustering Clustering High-Dimensional Data Semi-Supervised Learning and Active Learning

Constraint-Based Clustering Bi-Clustering and co-Clustering Collaborative filtering Spectral Clustering Evaluation of Clustering Quality Summary 3 Model-Based Clustering What is model-based clustering? Assumption: a cluster is generated by a model such as a probability distribution A model (e.g., Gaussian distribution) is determined by a set of parameters Task: optimize the fit between the given data and some mathematical models by learning the parameters of the

models Typical methods Statistical approach EM (Expectation maximization), AutoClass Neural network approach SOM (Self-Organizing Feature Map) 4 Mixture Models A cluster can be modeled as a probability distribution Practically, assume a distribution can be approximated well using multivariate normal distribution Multiple clusters is a mixture of different probability distributions A data set is a set of observations from a mixture of models 5 Object Probability Suppose there are k clusters and a set X of m objects Let the j-th cluster have parameter = ( , ) j

j j The probability that a point is in the j-th cluster is wj, where w1 + + wk = 1 The probability of an object x is k prob ( x | ) w j p j ( x | j ) j 1 m m k prob ( X | ) prob ( xi | ) w j p j ( xi | j ) i 1 i 1 j 1 6 Example 1 prob ( xi | ) e 2 ( x )2

2 2 1 ( 4,2) 2 (4,2) 1 prob ( x | ) e 2 2 ( x 4 ) 2 8 1 e 2 2 ( x 4)2 8 7 Maximal Likelihood Estimation Maximum likelihood principle: If we know a set of objects are from one distribution, but do not know the parameter, we can choose the parameter maximizing the probability ( x ) m 1

Maximize prob ( x | ) 2 e i 2 j 1 2 2 Equivalently, maximize ( xi ) 2 log prob ( X | ) 0.5m log 2 m log 2 2 i 1 m 8 The EM (Expectation Maximization) Algorithm Expectation Maximization algorithm Select an initial set of model parameters Repeat Expectation Step: For each object, calculate the probability that it belongs to each distribution i, i.e., prob(xi|i)

Maximization Step: Given the probabilities from the expectation step, find the new estimates of the parameters that maximize the expected likelihood Until the parameters are stable 9 Advantages and Disadvantages Mixture models are more general than k-means and fuzzy c-means Clusters can be characterized by a small number of parameters The results may satisfy the statistical assumptions of the generative models Computationally expensive Need large data sets Hard to estimate the number of clusters 10

Neural Network Approaches Neural network approaches Represent each cluster as an exemplar, acting as a prototype of the cluster New objects are distributed to the cluster whose exemplar is the most similar according to some distance measure Typical methods SOM (Soft-Organizing feature Map) Competitive learning Involves a hierarchical architecture of several units (neurons) Neurons compete in a winner-takes-all fashion for the object currently being presented 11 Self-Organizing Feature Map (SOM) SOMs, also called topological ordered maps, or Kohonen SelfOrganizing Feature Map (KSOMs) It maps all the points in a high-dimensional source space into a 2 to 3-d target space, s.t., the distance and proximity relationship (i.e., topology) are preserved as much as possible Similar to k-means: cluster centers tend to lie in a low-dimensional

manifold in the feature space Clustering is performed by having several units competing for the current object The unit whose weight vector is closest to the current object wins The winner and its neighbors learn by having their weights adjusted SOMs are believed to resemble processing that can occur in the brain Useful for visualizing high-dimensional data in 2- or 3-D space 12 Web Document Clustering Using SOM The result of SOM clustering of 12088 Web articles The picture on the right: drilling

down on the keyword mining Based on Web page 13 Chapter 11. Cluster Analysis: Advanced Methods Statistics-Based Clustering Clustering High-Dimensional Data Semi-Supervised Learning and Active Learning Constraint-Based Clustering Bi-Clustering and co-Clustering Collaborative filtering

Spectral Clustering Evaluation of Clustering Quality Summary 14 Clustering High-Dimensional Data Clustering high-dimensional data Many applications: text documents, DNA micro-array data Major challenges: Many irrelevant dimensions may mask clusters Distance measure becomes meaninglessdue to equi-distance Clusters may exist only in some subspaces Methods Feature transformation: only effective if most dimensions are relevant PCA & SVD useful only when features are highly correlated/redundant Feature selection: wrapper or filter approaches useful to find a subspace where the data have nice clusters

Subspace-clustering: find clusters in all the possible subspaces CLIQUE, ProClus, and frequent pattern-based clustering 15 The Curse of Dimensionality (graphs adapted from Parsons et al. KDD Explorations 2004) Data in only one dimension is relatively packed Adding a dimension stretch the points across that dimension, making them further apart Adding more dimensions will make the points further aparthigh dimensional data is extremely sparse Distance measure becomes meaninglessdue to equi-distance 16 Why Subspace Clustering? (adapted from Parsons et al. SIGKDD Explorations 2004)

Clusters may exist only in some subspaces Subspace-clustering: find clusters in all the subspaces 17 CLIQUE (Clustering In QUEst) Agrawal, Gehrke, Gunopulos, Raghavan (SIGMOD98) Automatically identifying subspaces of a high dimensional data space that allow better clustering than original space CLIQUE can be considered as both density-based and grid-based It partitions each dimension into the same number of equal length interval It partitions an m-dimensional data space into non-overlapping rectangular units A unit is dense if the fraction of total data points contained in the unit exceeds the input model parameter

A cluster is a maximal set of connected dense units within a subspace 18 CLIQUE: The Major Steps Partition the data space and find the number of points that lie inside each cell of the partition. Identify the subspaces that contain clusters using the Apriori principle Identify clusters Determine dense units in all subspaces of interests Determine connected dense units in all subspaces of interests. Generate minimal description for the clusters Determine maximal regions that cover a cluster of connected dense units for each cluster Determination of minimal cover for each cluster 19

=3 30 40 Vacation 20 50 Salary (10,000) 0 1 2 3 4 5 6 7 la a S ry 30 Vacation (week) 0 1 2 3 4 5 6 7 age 60 20 50 30 40 50 age 60 age

20 Strength and Weakness of CLIQUE Strength automatically finds subspaces of the highest dimensionality such that high density clusters exist in those subspaces insensitive to the order of records in input and does not presume some canonical data distribution scales linearly with the size of input and has good scalability as the number of dimensions in the data increases Weakness The accuracy of the clustering result may be degraded at the expense of simplicity of the method 21 Frequent Pattern-Based Approach Clustering high-dimensional space (e.g., clustering text documents, microarray data) Projected subspace-clustering: which dimensions to be projected on?

CLIQUE, ProClus Feature extraction: costly and may not be effective? Using frequent patterns as features Clustering by pattern similarity in micro-array data (pClustering) [H. Wang, W. Wang, J. Yang, and P. S. Yu. Clustering by pattern similarity in large data sets, SIGMOD02] February 9, 2020 Data Mining: Concepts and Techniques 22 Clustering by Pattern Similarity (pClustering) Left figure: Micro-array raw data shows 3 genes and their values in a multi-D space: Difficult to find their patterns Right two: Some subsets of dimensions form nice shift and scaling patterns No globally defined similarity/distance measure

Clusters may not be exclusive An object can appear in multiple clusters 23 Why p-Clustering? Microarray data analysis may need to Clustering on thousands of dimensions (attributes) Discovery of both shift and scaling patterns Clustering with Euclidean distance measure? cannot find shift patterns Clustering on derived attribute Aij = ai aj? introduces N(N-1) dimensions Bi-cluster (Y. Cheng and G. Church. Biclustering of expression data. ISMB00) using transformed mean-squared residue score matrix (I, J) 1 d d ij | J | ij

jJ d 1 d | I | i I ij d Where A submatrix is a -cluster if H(I, J) for some > 0 Ij IJ 1 d | I || J | i I , j J ij Problems with bi-cluster No downward closure property

Due to averaging, it may contain outliers but still within -threshold February 9, 2020 Data Mining: Concepts and Techniques 24 p-Clustering: Clustering by Pattern Similarity P-score: the similarity between two objects r x, ry on two attributes au, av d xa d xb pScore ( ) | (d xa d xb ) (d ya d yb ) | d ya d yb -pCluster: If for any 2 by 2 matrix X, pScore(X) ( > 0) Properties of -pCluster Downward closure

Clusters are more homogeneous than bi-cluster (thus the name: pair-wise Cluster) MaPle (Pei et al. 2003): Efficient mining of maximum p-clusters For scaling patterns, taking logarithmic on d xa / d ya d xb / d yb to the pScore form will lead Chapter 11. Cluster Analysis: Advanced Methods Statistics-Based Clustering Clustering High-Dimensional Data Semi-Supervised Learning and Active Learning Constraint-Based Clustering

Bi-Clustering and co-Clustering Collaborative filtering Spectral Clustering Evaluation of Clustering Quality Summary 31 Why Constraint-Based Cluster Analysis? Need user feedback: Users know their applications the best Less parameters but more user-desired constraints, e.g., an ATM allocation problem: obstacle & desired clusters 32 A Classification of Constraints in Cluster Analysis

Clustering in applications: desirable to have user-guided (i.e., constrained) cluster analysis Different constraints in cluster analysis: Constraints on individual objects (do selection first) Constraints on distance or similarity functions # of clusters, MinPts, etc. User-specified constraints Weighted functions, obstacles (e.g., rivers, lakes) Constraints on the selection of clustering parameters Cluster on houses worth over $300K Contain at least 500 valued customers and 5000 ordinary ones Semi-supervised: giving small training sets as constraints or hints

33 Clustering With Obstacle Objects Tung, Hou, and Han. Spatial Clustering in the Presence of Obstacles, ICDE'01 K-medoids is more preferable since k-means may locate the ATM center in the middle of a lake Visibility graph and shortest path Triangulation and micro-clustering Two kinds of join indices (shortestpaths) worth pre-computation VV index: indices for any pair of obstacle vertices MV index: indices for any pair of micro-cluster and obstacle indices 34 An Example: Clustering With Obstacle Objects Not Taking obstacles into account Taking obstacles into account 35

User-Guided Clustering person name course course-id group office semester name position instructor area Advise professor name student area degree User hint

Target of clustering Course Professor Group Open-course Work-In Publish Publication author title title year conf Register student Student

course name office semester position unit grade X. Yin, J. Han, P. S. Yu, Cross-Relational Clustering with User's Guidance, KDD'05 User usually has a goal of clustering, e.g., clustering students by research area User specifies his clustering goal to CrossClus 36 Comparing with Classification User hint User-specified feature (in the form of attribute) is used as a hint, not class labels The attribute may contain too many or too few distinct values, e.g., a user may want to cluster students into 20 clusters instead of 3

All tuples for clustering Additional features need to be included in cluster analysis 37 Comparing with Semi-Supervised Clustering Semi-supervised clustering: User provides a training set consisting of similar (must-link) and dissimilar (cannot link) pairs of objects User-guided clustering: User specifies an attribute as a hint, and more relevant features are found for clustering User-guided clustering All tuples for clustering Semi-supervised clustering x All tuples for clustering 38 Why Not Semi-Supervised Clustering?

Much information (in multiple relations) is needed to judge whether two tuples are similar A user may not be able to provide a good training set It is much easier for a user to specify an attribute as a hint, such as a students research area Tom Smith Jane Chang SC1211 BI205 TA RA Tuples to be compared User hint 39 CrossClus: An Overview Measure similarity between features by how they group objects into clusters Use a heuristic method to search for pertinent features Use tuple ID propagation to create feature values

Start from user-specified feature and gradually expand search range Features can be easily created during the expansion of search range, by propagating IDs Explore three clustering algorithms: k-means, k-medoids, and hierarchical clustering 40 Multi-Relational Features A multi-relational feature is defined by: A join path, e.g., Student Register OpenCourse Course An attribute, e.g., Course.area (For numerical feature) an aggregation operator, e.g., sum or average Categorical feature f = [Student Register OpenCourse Course, Course.area, null] areas of courses of each student Tuple Areas of courses DB AI TH t1 5

5 0 t2 0 3 t3 1 t4 t5 Values of feature f Tuple Feature f DB AI TH t1 0.5 0.5 0

7 t2 0 0.3 0.7 5 4 t3 0.1 0.5 0.4 5 0 5 t4 0.5 0 0.5

3 3 4 t5 0.3 0.3 0.4 f(t1) f(t2) f(t3) f(t4) DB AI TH f(t5) 41 Representing Features Similarity between tuples t1 and t2 w.r.t. categorical feature f Cosine similarity between vectors f(t1) and f(t2) L

f t . p 1 sim f t1 , t 2 L 1 k 1 f t 2 . pk k 1 f t . p Similarity vector Vf k 2 k L

f t . p 2 2 k k 1 Most important information of a feature f is how f groups tuples into clusters f is represented by similarities between every pair of tuples indicated by f The horizontal axes are the tuple indices, and the vertical axis is the similarity This can be considered as a vector of N x N dimensions 42 Similarity Between Features Vf Values of Feature f and g Feature f (course) Feature g (group) DB AI TH Info sys

Cog sci Theory t1 0.5 0.5 0 1 0 0 t2 0 0.3 0.7 0 0 1 t3 0.1

0.5 0.4 0 0.5 0.5 t4 0.5 0 0.5 0.5 0 0.5 t5 0.3 0.3 0.4 0.5 0.5

0 Vg Similarity between two features cosine similarity of two vectors V f V g sim f , g f g V V 43 Computing Feature Similarity Feature f Tuples Feature g DB Info sys AI Cog sci TH Theory N N Similarity between feature

values w.r.t. the tuples sim(fk,gq)=i=1 to N f(ti).pkg(ti).pq Info sys DB l 2 m ti , t j simg ti , t j fsimilarities, V f V g Tuple sim fsimilarities, Featuresim value k , gq i 1 j 1 hard to compute DB Info sys AI Cog sci TH Theory k 1 q easy 1

to compute Compute similarity between each pair of feature values by one scan on data 44 Searching for Pertinent Features Different features convey different aspects of information Academic Performances Research area Research group area Conferences of papers Advisor Demographic info GPA Permanent address GRE score Nationality Number of papers

Features conveying same aspect of information usually cluster tuples in more similar ways Research group areas vs. conferences of publications Given user specified feature Find pertinent features by computing feature similarity 45 Heuristic Search for Pertinent Features Overall procedure Course Professor person name course course-id group office semester name position instructor area

2 1. Start from the userGroup specified feature name 2. Search in neighborhood area of existing pertinent features User hint 3. Expand search range gradually Target of clustering Open-course Work-In Advise Publish professor student degree author 1 title Publication title year

conf Register student Student name office position course semester unit grade Tuple ID propagation is used to create multi-relational features IDs of target tuples can be propagated along any join path, from which we can find tuples joinable with each target tuple 46 Clustering with Multi-Relational Features Given a set of L pertinent features f1, , fL, similarity between two tuples L sim t1 , t 2 sim f i t1 , t 2 f i .weight i 1 Weight of a feature is determined in feature search by its similarity with other pertinent features

Clustering methods CLARANS [Ng & Han 94], a scalable clustering algorithm for non-Euclidean space K-means Agglomerative hierarchical clustering 47 Experiments: Compare CrossClus with Baseline: Only use the user specified feature PROCLUS [Aggarwal, et al. 99]: a state-of-the-art subspace clustering algorithm Use a subset of features for each cluster We convert relational database to a table by propositionalization User-specified feature is forced to be used in every cluster RDBC [Kirsten and Wrobel00] A representative ILP clustering algorithm Use neighbor information of objects for clustering User-specified feature is forced to be used 48

Measure of Clustering Accuracy Accuracy Measured by manually labeled data We manually assign tuples into clusters according to their properties (e.g., professors in different research areas) Accuracy of clustering: Percentage of pairs of tuples in the same cluster that share common label This measure favors many small clusters We let each approach generate the same number of clusters 49 DBLP Dataset Clustering Accurarcy - DBLP 1 0.9 0.8 0.7 CrossClus K-Medoids CrossClus K-Means CrossClus Agglm

Baseline PROCLUS RDBC 0.6 0.5 0.4 0.3 0.2 0.1 0 nf o C W d or r ho t au o C f+ n Co W d or f+

n Co or h ut a Co W d or + au o C or h t ll A re th e 50 Summary

Cluster analysis groups objects based on their similarity and has wide applications Measure of similarity can be computed for various types of data Clustering algorithms can be categorized into partitioning methods, hierarchical methods, density-based methods, grid-based methods, and model-based methods Outlier detection and analysis are very useful for fraud detection, etc. and can be performed by statistical, distance-based or deviation-based approaches There are still lots of research issues on cluster analysis 61 Problems and Challenges Considerable progress has been made in scalable clustering methods Partitioning: k-means, k-medoids, CLARANS

Hierarchical: BIRCH, ROCK, CHAMELEON Density-based: DBSCAN, OPTICS, DenClue Grid-based: STING, WaveCluster, CLIQUE Model-based: EM, Cobweb, SOM Frequent pattern-based: pCluster Constraint-based: COD, constrained-clustering Current clustering techniques do not address all the requirements adequately, still an active area of research 62 References

G. J. McLachlan and K.E. Bkasford. Mixture Models: Inference and Applications to Clustering. John Wiley and Sons, 1988. P. Michaud. Clustering Techniques. Future Generation Computer Systems, 13, 1997. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Printice Hall, 1988. L. Parsons, E. Haque and H. Liu, Subspace Clustering for High Dimensional Data: A Review, SIGKDD Explorations, 6(1), June 2004 E. Schikuta. Grid clustering: An efficient hierarchical clustering method for very large data sets. Proc. 1996 Int. Conf. on Pattern Recognition,. A. K. H. Tung, J. Han, L. V. S. Lakshmanan, and R. T. Ng. Constraint-Based Clustering in Large Databases, ICDT'01. A. K. H. Tung, J. Hou, and J. Han. Spatial Clustering in the Presence of Obstacles, ICDE'01 H. Wang, W. Wang, J. Yang, and P.S. Yu. Clustering by pattern similarity in large data sets, SIGMOD 02. X. Yin, J. Han, and P.S. Yu, Cross-Relational Clustering with User's Guidance, in Proc. 2005 Int. Conf. on Knowledge Discovery and Data Mining (KDD'05), Chicago, IL, Aug. 2005. 63 64 Slides Not to Be Used in Class 65 Chapter 11. Cluster Analysis: Advanced

Methods Statistics-Based Clustering Model-Based Clustering: The Expectation-Maximization Method Neural Network Approach (SOM) Fuzzy and non-crisp clustering Clustering High-Dimensional Data Why Subspace Clustering?Challenges on Clustering High-Dimensional Data PROCLUS: A Dimension-Reduction Subspace Clustering Method Frequent Pattern-Based Clustering Methods Semi-Supervised Learning and Active Learning Semi-supervised clustering Classification of partially labeled data Constraint-Based and User-Guided Cluster Analysis

Clustering with Obstacle Objects User-Constrained Cluster Analysis User-Guided Cluster Analysis Bi-Clustering and co-Clustering Collaborative filtering Clustering-based approach Classification-Based Approach Frequent Pattern-Based Approach Spectral Clustering Evaluation of Clustering Quality Summary 66 Conceptual Clustering Conceptual clustering A form of clustering in machine learning Produces a classification scheme for a set of unlabeled objects Finds characteristic description for each concept (class) COBWEB (Fisher87) A popular a simple method of incremental conceptual learning Creates a hierarchical clustering in the form of a classification tree Each node refers to a concept and contains a

probabilistic description of that concept 67 COBWEB Clustering Method A classification tree 68 More on Conceptual Clustering Limitations of COBWEB The assumption that the attributes are independent of each other is often too strong because correlation may exist Not suitable for clustering large database data skewed tree and expensive probability distributions CLASSIT an extension of COBWEB for incremental clustering of continuous data suffers similar problems as COBWEB

AutoClass (Cheeseman and Stutz, 1996) Uses Bayesian statistical analysis to estimate the number of clusters Popular in industry 69

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