Data Warehousing and Data Mining

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1 Data Warehousing and Data Mining Lecture Clustering Methods CITS CITS Wei Liu School of Computer Science and Software Engineering Faculty of Engineering, Computing and Mathematics Acknowledgement: The Lecture Slides are adapted from the original slides of Han s textbook.

2 Lecture Outline Partition-based Clustering K-Means Algorithm K-Mediods Algorithm Density-Based Clustering DBScan

3 Major Clustering Approaches (I) Partitioning approach: k-means, k-medoids, CLARANS Construct k-partitions for the given n-objects (k n). Each group contains at least one object. Each object must belong to exactly one group. Hierarchical approach: Diana, Agnes, BIRCH, ROCK, CAMELEON Create a hierarchical decomposition of the set of objects using some criterion (linkage function ) Agglomerative Approach: bottom-up merging Divisive Approach: top-down splitting Density-based approach: DBSACN, OPTICS, DenClue Based on connectivity and density functions. i.e., for each data point within a given cluster, the radius of a given cluster has to contain at least a minimum number of points.

4 Major Clustering Approaches (II) Grid-based approach: based on a multiple-level granularity structure Typical methods: STING, WaveCluster, CLIQUE Model-based: A model is hypothesized for each of the clusters and tries to find the best fit of that model to each other Typical methods: EM, SOFM, COBWEB Frequent pattern-based: Based on the analysis of frequent patterns Typical methods: pcluster User-guided or constraint-based: Clustering by considering user-specified or application-specific constraints Typical methods: COD (obstacles), constrained clustering

5 Calculate the distance between Clusters Single link smallest distance between an element in one cluster and an element in the other, i.e., dis K i, K j = min(t ip, t jp ) Complete link largest distance between an element in one cluster and an element in the other, i.e., dis K i, K j = max(t ip, t jp ) Average avg distance between an element in one cluster and an element in the other, i.e., dis K i, K j = avg(t ip, t jp ) Centroid distance between the centroids of two clusters, i.e., dis K i, K j = dis(c i, C j ) Medoid: medoid is the most centrally located object in a cluster distance between the medoids of two clusters, i.e., dis K i, K j = dis(m i, M j )

6 Centroid, Radius and Diameter of a Cluster (for numerical data sets) Centroid: the middle of a cluster C m = i= N tip N Radius: square root of average distance from any point of the cluster to its centroid R m = i= N (t ip c m ) N Diameter: square root of average mean squared distance between all pairs of points in the cluster D m = i= N i= N (t ip t iq ) N(N )

7 Partitioning Algorithms: Basic Concept Partitioning method: Construct a partition of a database D of n objects into a set of k clusters, s.t., min sum of squared distance (within cluster variance) k E = i= p Ci (p m i ) 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 ): Each cluster is represented by the center of the cluster k-medoids or PAM (Partition around medoids) (Kaufman & Rousseeuw ): Each cluster is represented by one of the objects in the cluster

8 k-means: A centroid-based partitioning algorithm Given k, the k-means algorithm is implemented in four steps: Partition objects into k nonempty subsets 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) Assign each object to the cluster with the nearest seed point Go back to Step, stop when no more new assignment

9 Example: the k-means Clustering Method Assign each objects to most similar center reassign Update the cluster means reassign K= Arbitrarily choose K object as initial cluster center Update the cluster means

10 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. Comparing: PAM: O(k(n k) ), CLARA: O(ks + k(n k)) Comment: Often terminates at a local optimum. The global optimum may be found using techniques such as: deterministic annealing and genetic algorithms 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

11 Variations of k-means 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 ) 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

12 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.

13 The K-Medoids Clustering Method Find representative objects, called medoids, in clusters PAM (Partitioning Around Medoids, ) 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, ) CLARANS (Ng & Han, ): Randomized sampling Focusing + spatial data structure (Ester et al., )

14 A Typical K-Medoids Algorithm (PAM) Arbitrary choose k object as initial medoids Assign each remainin g object to nearest medoids K= Total Cost = Randomly select a nonmedoid object,o ramdom Do loop Until no change Swapping O and O ramdom If quality is improved. Compute total cost of swapping

15 PAM Algorithm PAM (Kaufman and Rousseeuw, ), built in S+ Use real object to represent the cluster Select k representative objects arbitrarily For each pair of non-selected object h and selected object i, calculate the total swapping cost TC ih For each pair of i and h, If TC ih <, i is replaced by h Then assign each non-selected object to the most similar representative object repeat steps - until there is no change

16 Determining O random a good replacement of O j We calculate the distance from every object p to the closest object in the set {o,, o j, o random, o j+,, ok}, Then use the distance to update the cost function. Suppose object p is currently assigned to a cluster represented by medoid o j (Figure a or b). Do we need to reassign p to a different cluster if o j is being replaced by o random? What if object p is currently assigned to a different cluster represented by medoid o i? (Figure c or d)

17 PAM Clustering: Total Swapping Cost: TC ih = j C jih j i h t C jih = t i h j C jih = d(j, h) - d(j, i) h i t j C jih = d(j, t) - d(j, i) t i h j C jih = d(j, h) - d(j, t)

18 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) ) for each iteration where n is # of data,k is # of clusters Sampling based method, CLARA(Clustering LARge Applications)

19 CLARA (Clustering Large Applications) () CLARA (Kaufmann and Rousseeuw in ) Built in statistical analysis packages, such as S+ It draws multiple samples of the data set, applies PAM on each sample, and gives the best clustering as the output 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

20 CLARANS ( Randomized CLARA) () CLARANS (A Clustering Algorithm based on Randomized Search) (Ng and Han ) 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 If the local optimum is found, CLARANS starts with new randomly selected node in search for a new local optimum It is more efficient and scalable than both PAM and CLARA Focusing techniques and spatial access structures may further improve its performance (Ester et al. )

21 Density-Based Clustering Algorithms 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 ) OPTICS: Ankerst, et al (SIGMOD ). DENCLUE: Hinneburg & D. Keim (KDD ) CLIQUE: Agrawal, et al. (SIGMOD ) (more grid-based)

22 Density Based Clustering: Basic Concepts Two parameters: Eps: Maximum radius of the neighbourhood MinPts: Minimum number of points in an Eps-neighbourhood of that point N Eps p : q D dist(p, q) Eps} Directly density-reachable: A point p is directly density-reachable from a point q w.r.t. Eps, MinPts if p belongs to N Eps p & q is a core. Core point condition: N Eps (q) MinPts q p MinPts = Eps = cm

23 Density-Reachable and Density-Connected Density-reachable: A point p is density-reachable from a point q w.r.t. Eps, MinPts if there is a chain of points p,, p n, p = q, p n = p such that p i+ is directly density-reachable from p i Density-connected q p p A point p is density-connected to a point q w.r.t. Eps, MinPts if there is a point o such that both, p and q are density-reachable from o w.r.t. Eps and MinPts p q o

24 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 densityconnected points Discovers clusters of arbitrary shape in spatial databases with noise Outlier Border Core Eps = cm MinPts =

25 DBSCAN: The Algorithm Arbitrary select a point p Retrieve all points density-reachable from p w.r.t. 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.

26 DBSCAN: Sensitive to Parameters

27 Extensions of DBSCAN OPTICS: (Ordering Points To Identify the Clustering Structure) Produces cluster ordering obtained for wide range of parameter setting in the framework of density-based clustering structure. core-distance, reachability-distance. Good for both automatic and interactive cluster analysis, including finding intrinsic clustering structure Can be represented graphically or using visualization techniques DENCLUE: Clusters object based on set of density distribution functions. Each point is modelled using a mathematical function called influence function, which describes the influence of the function within its neighborhood. Data space density is modelled as sum of individual influence function. Clusters are then determined by identifying density attractors, which are nothing but local maxima.

28 Summary Cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. Cluster analysis can be used as a stand-alone data mining tool to gain insight into the data distribution or can serve as a preprocessing step for other data mining algorithms operated on the detected clusters. The quality of cluster is based on a measure of dissimilarity of objects, computed for various types of data (interval-scaled, binary, categorical, ordinal and ratio scaled). Cosine measure and Tanimoto coefficients are used for nonmetric vector data. Partitioning Method: iterative relocation technique- k-means, k- medoids, CLARANS, etc. K-medoid is efficient in presence of noise and outliers and CLARANS is its extension for working with large data sets. Desnsity Based Method Discover clusters of arbitrary shape, good at handling noise, requires only one scan But sensitive to density parameters, which are required as termination condition

29 Overview of Clustering Methods Partitioning methods Hierarchical methods Density-based methods Grid-based methods General Characteristics Find mutually exclusive clusters of spherical shape Distance-based May use mean or medoid (etc.) to represent cluster center Effective for small- to medium-size data sets Clustering is a hierarchical decomposition (i.e., multiple levels) Cannot correct erroneous merges or splits May incorporate other techniques like microclustering or consider object linkages Can find arbitrarily shaped clusters Clusters are dense regions of objects in space that are separated by low-density regions Cluster density: Each point must have a minimum number of points within its neighborhood May filter out outliers Use a multiresolution grid data structure Fast processing time (typically independent of the number of data objects, yet dependent on grid size)

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