1. 1 Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Density-Based Methods Outlier Analysis Summary

2. 2 What is Cluster Analysis? Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Cluster analysis Grouping a set of data objects into clusters Clustering is unsupervised learning: no predefined classes Typical applications As a stand-alone tool to get insight into data distribution As a preprocessing step for other algorithms

3. 3 General Applications of Clustering Pattern Recognition Spatial Data Analysis create maps in GIS by clustering feature spaces detect spatial clusters and explain them in spatial data mining Image Processing Economic Science (especially market research) WWW Document classification Cluster Weblog data to discover groups of similar access patterns

4. 4 Examples of Clustering Applications Marketing: Help marketers discover distinct groups in their customer bases, and then use this knowledge to develop targeted marketing programs Land use: Identification of areas of similar land use in an earth observation database Insurance: Identifying groups of motor insurance policy holders with a high average claim cost City-planning: Identifying groups of houses according to their house type, value, and geographical location Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults

5. 5 What Is Good Clustering? A good clustering method will produce high quality clusters with high intra-class similarity low inter-class similarity The quality of a clustering result depends on both the similarity measure used by the method and its implementation. The quality of a clustering method is also measured by its ability to discover some or all of the hidden patterns.

6. 6 Requirements of Clustering in Data Mining Scalability Ability to deal with different types of attributes Discovery of clusters with arbitrary shape Minimal requirements for domain knowledge to determine input parameters Able to deal with noise and outliers Insensitive to order of input records High dimensionality Incorporation of user-specified constraints Interpretability and usability

7. 7 Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Density-Based Methods Outlier Analysis Summary

8. 8 Measure the Quality of Clustering Dissimilarity/Similarity metric: Similarity is expressed in terms of a distance function, which is typically metric: d(i, j) There is a separate “quality” function that measures the “goodness” of a cluster. The definitions of distance functions are usually very different for interval-scaled, boolean, categorical, ordinal and ratio variables. Weights should be associated with different variables based on applications and data semantics. It is hard to define “similar enough” or “good enough” the answer is typically highly subjective.

9. 9 Type of data in clustering analysis Interval-scaled variables: Binary variables: Nominal, ordinal, and ratio variables: Variables of mixed types:

10. 10 Interval-valued variables Standardize data Calculate the mean absolute deviation: where Calculate the standardized measurement (z-score) Using mean absolute deviation is more robust than using standard deviation

11. 11 Similarity and Dissimilarity Between Objects Distances are normally used to measure the similarity or dissimilarity between two data objects Some popular ones include: Minkowski distance: where i = (x i1, x i2, …, x ip ) and j = (x j1, x j2, …, x jp ) are two p-dimensional data objects, and q is a positive integer If q = 1, d is Manhattan distance

12. 12 Similarity and Dissimilarity Between Objects If q = 2, d is Euclidean distance: Properties d(i,j)  0 d(i,i) = 0 d(i,j) = d(j,i) d(i,j)  d(i,k) + d(k,j) Also, one can use weighted distance, parametric Pearson product moment correlation, or other dissimilarity measures

13. 13 Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Density-Based Methods Outlier Analysis Summary

14. 14 Major Clustering Approaches Partitioning algorithms: Construct various partitions and then evaluate them by some criterion Density-based: based on connectivity and density functions

15. 15 Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Density-Based Methods Outlier Analysis Summary

16. 16 Partitioning Algorithms: Basic Concept Partitioning method: Construct a partition of a database D of n objects into a set of k clusters Given a k, find a partition of k clusters that optimizes the chosen partitioning criterion Global optimal: exhaustively enumerate all partitions Heuristic methods: k-means and k-medoids algorithms k-means (MacQueen’67): Each cluster is represented by the center of the cluster k-medoids or PAM (Partition around medoids) (Kaufman & Rousseeuw’87): Each cluster is represented by one of the objects in the cluster

17. 17 The K-Means Clustering Method Given k, the k-means algorithm is implemented in four steps: 1. Partition objects into k nonempty subsets 2. Compute seed points as the centroids of the clusters of the current partition (the centroid is the center, i.e., mean point, of the cluster) 3. Assign each object to the cluster with the nearest seed point 4. Go back to Step 2, stop when no more new assignment

18. 18 The K-Means Clustering Method Example 0 1 2 3 4 5 6 7 8 9 10 0123456789 0 1 2 3 4 5 6 7 8 9 0123456789 K=2 Arbitrarily choose K object as initial cluster center Assign each objects to most similar center Update the cluster means reassign

19. 19 Comments on the K-Means Method Strength: Relatively efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n. Weakness Applicable only when mean is defined, then what about categorical data? Need to specify k, the number of clusters, in advance Unable to handle noisy data and outliers Not suitable to discover clusters with non-convex shapes

20. 20 Variations of the K-Means Method A few variants of the k-means which differ in Selection of the initial k means Dissimilarity calculations Strategies to calculate cluster means Handling categorical data: k-modes (Huang’98) Replacing means of clusters with modes Using new dissimilarity measures to deal with categorical objects Using a frequency-based method to update modes of clusters A mixture of categorical and numerical data: k-prototype method

21. 21 What is the problem of k-Means Method? The k-means algorithm is sensitive to outliers ! Since an object with an extremely large value may substantially distort the distribution of the data. K-Medoids: Instead of taking the mean value of the object in a cluster as a reference point, medoids can be used, which is the most centrally located object in a cluster. 0 1 2 3 4 5 6 7 8 9 10 0123456789 0 1 2 3 4 5 6 7 8 9 0123456789

22. 22 The K-Medoids Clustering Method Find representative objects, called medoids, in clusters PAM (Partitioning Around Medoids, 1987) starts from an initial set of medoids and iteratively replaces one of the medoids by one of the non-medoids if it improves the total distance of the resulting clustering PAM works effectively for small data sets, but does not scale well for large data sets CLARA (Kaufmann & Rousseeuw, 1990) CLARANS (Ng & Han, 1994): Randomized sampling

23. 23 Typical k-medoids algorithm (PAM) Total Cost = 20 0 1 2 3 4 5 6 7 8 9 10 0123456789 K=2 Arbitrary choose k object as initial medoids Assign each remaining object to nearest medoids Randomly select a nonmedoid object,O ramdom Compute total cost of swapping 0 1 2 3 4 5 6 7 8 9 10 0123456789 Total Cost = 26 Swapping O and O ramdom If quality is improved. Do loop Until no change 0 1 2 3 4 5 6 7 8 9 10 0123456789

24. 24 PAM (Partitioning Around Medoids) (1987) Use real object to represent the cluster 1. Select k representative objects arbitrarily 2. For each pair of non-selected object h and selected object i, calculate the total swapping cost TC ih PAM computes cost C jih for all non-selected objects O j 3. For each pair of i and h, If TC ih < 0, i is replaced by h Then assign each non-selected object to the most similar representative object 4. repeat steps 2-3 until there is no change

25. 25 PAM (Partitioning Around Medoids) (1987) First case, Suppose O j currently belongs to the cluster represented by O i. Furthermore, let O j be more similar to O j,2 than O h, i.e., d(O j, O h ) > d(O j, O j,2 ), where O j,2 is the second most similar medoid to O j. C jih = d(O j, O j,2 ) - d(O j, O i )

26. 26 PAM (Partitioning Around Medoids) (1987) Second case, O j currently belongs to the cluster represented by O i. But this time, O j is less similar to O j,2 than O h, i.e., d(O j, O h ) < d(O j, O j,2 ). C jih = d(O j, O h ) - d(O j, O i )

27. 27 PAM (Partitioning Around Medoids) (1987) Third case, Suppose that O j currently belongs to a cluster (represented by O j2 ) other than the one represented by O i. But O j is more similar to O j,2 than O h. C jih = 0.

28. 28 PAM (Partitioning Around Medoids) (1987) Fourth case, O j currently belongs to the cluster represented by O j,2. But O j is less similar to O j,2 than O h. C jih = d(O j, O h ) - d(O j, O j,2 )

29. 29 PAM (Partitioning Around Medoids) (1987) Combining the four cases above, the total cost of replacing Oi with Oh is given by: TC ih =  j C jih

30. 30 What is the problem with PAM? PAM is more robust than k-means in the presence of noise and outliers because a medoid is less influenced by outliers or other extreme values than a mean PAM works efficiently for small data sets but does not scale well for large data sets. O(k(n-k) 2 ) for each iteration where n is # of data,k is # of clusters  Sampling based method, CLARA(Clustering LARge Applications)

31. 31 CLARA (Clustering Large Applications) CLARA (Kaufmann and Rousseeuw in 1990) It draws multiple samples of the data set, applies PAM on each sample, and gives the best clustering as the output

32. 32 CLARA (Clustering Large Applications) 1. For i = 1 to 5, repeat the following steps: 2. Draw a sample of 40 + 2k objects randomly from the entire data set, and call Algorithm PAM to find k medoids of the sample. 3. For each object O j in the entire data set, determine which of the k medoids is the most similar to O j. 4. Calculate the average dissimilarity of the clustering obtained in the previous step. If the value is less than the current minimum, use this value as the current minimum, and retain the k medoids found in Step (2) as the best set of medoids obtained so far. 5. Return to Step (1) to start the next iteration.

33. 33 CLARA (Clustering Large Applications) Strength: deals with larger data sets than PAM Weakness: Efficiency depends on the sample size A good clustering based on samples will not necessarily represent a good clustering of the whole data set if the sample is biased CLARA may miss good solutions

34. 34 CLARANS (“Randomized” CLARA) CLARANS (A Clustering Algorithm based on Randomized Search) (Ng and Han’94) CLARANS draws sample of neighbors dynamically The clustering process can be presented as searching a graph where every node is a potential solution, that is, a set of k medoids Each node is represented by a set of k objects Two nodes are neighbors if their sets differ by one object It is more efficient and scalable than both PAM and CLARA

35. 35 CLARANS (“Randomized” CLARA) 1. Input parameters numlocal and maxneighbor. Initialize i to 1, and mincost to a large number. 2. Set current to an arbitrary node in G n,k. 3. Set j to 1. 4. Consider a random neighbor S of current, and based on equation TC ih =  j C jih, calculate the cost differential of the two nodes. 5. If S has a lower cost, set current to S, and go to Step (3). 6. Otherwise, increment j by 1. If j ≤ maxneighbor, go to Step (4). 7. Otherwise, when j > maxneighbor, compare the cost of current with mincost. If the former is less than mincost, set mincost to the cost of current, and set bestnode to current. 8. Increment i by 1. If i > numlocal, output bestnode and halt. Otherwise, go to Step (2).

36. 36 Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Density-Based Methods Outlier Analysis Summary

37. 37 Density-Based Clustering Methods Clustering based on density (local cluster criterion), such as density-connected points Major features: Discover clusters of arbitrary shape Handle noise One scan Need density parameters as termination condition Several interesting studies: DBSCAN: Ester, et al. (KDD ’ 96) OPTICS: Ankerst, et al (SIGMOD ’ 99). DENCLUE: Hinneburg & D. Keim (KDD ’ 98) CLIQUE: Agrawal, et al. (SIGMOD ’ 98)

38. 38 Density Concepts Core object (CO)–object with at least ‘M’ objects within a radius ‘E-neighborhood’ Directly density reachable (DDR)–x is CO, y is in x’s ‘E- neighborhood’ Density reachable–there exists a chain of DDR objects from x to y Density based cluster–density connected objects maximum w.r.t. reachability

39. 39 Density-Based Clustering: Background Two parameters: Eps: Maximum radius of the neighbourhood MinPts: Minimum number of points in an Eps- neighbourhood of that point N Eps (p): {q belongs to D | dist(p,q) <= Eps} Directly density-reachable: A point p is directly density- reachable from a point q wrt. Eps, MinPts if 1) p belongs to N Eps (q) 2) core point condition: |N Eps (q)| >= MinPts

40. 40 Density-Based Clustering: Background Density-reachable: A point p is density-reachable from a point q wrt. Eps, MinPts if there is a chain of points p 1, …, p n, p 1 = q, p n = p such that p i+1 is directly density-reachable from p i Density-connected A point p is density-connected to a point q wrt. Eps, MinPts if there is a point o such that both, p and q are density-reachable from o wrt. Eps and MinPts. p q p1p1 pq o

41. 41 DBSCAN: Density Based Spatial Clustering of Applications with Noise Relies on a density-based notion of cluster: A cluster is defined as a maximal set of density-connected points Discovers clusters of arbitrary shape in spatial databases with noise Core Border Outlier Eps = 1cm MinPts = 5

42. 42 DBSCAN: The Algorithm Arbitrary select a point p Retrieve all points density-reachable from p wrt Eps and MinPts. If p is a core point, a cluster is formed. If p is a border point, no points are density-reachable from p and DBSCAN visits the next point of the database. Continue the process until all of the points have been processed.

43. 43 Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Density-Based Methods Outlier Analysis Summary

44. 44 What Is Outlier Discovery? What are outliers? The set of objects are considerably dissimilar from the remainder of the data Example: Sports: Michael Jordon Problem Find top n outlier points Applications: Credit card fraud detection Telecom fraud detection Customer segmentation Medical analysis

45. 45 Outlier Discovery: Statistical Approaches Assume a model underlying distribution that generates data set (e.g. normal distribution) Use discordancy tests depending on data distribution distribution parameter (e.g., mean, variance) number of expected outliers Drawbacks most tests are for single attribute In many cases, data distribution may not be known

46. 46 Outlier Discovery: Distance-Based Approach Introduced to counter the main limitations imposed by statistical methods We need multi-dimensional analysis without knowing data distribution. Distance-based outlier: A DB(p, D)-outlier is an object O in a dataset T such that at least a fraction p of the objects in T lies at a distance greater than D from O

47. 47 Cluster Analysis What is Cluster Analysis? Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Partitioning Methods Density-Based Methods Outlier Analysis Summary

48. 48 Summary Cluster analysis groups objects based on their similarity and has wide applications Measure of similarity can be computed for various types of data 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, such as constraint-based clustering