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Clustering Overview Today’s lecture • What is clustering • Partitional algorithms: K-means Clustering Techniques (1) Next lecture • Hierarchical algorithms • Density-based algorithms: DBSCAN • Techniques for clustering large databases – BIRCH – CURE Data Mining: Clustering 2 What is Cluster Analysis? Applications of Cluster Analysis • Finding groups of objects such that objects in a group are similar (or related) to one another and different from (or unrelated to) the objects in other groups • Understanding Inter-cluster distances are maximized Intra-cluster distances are minimized Discovered Clusters – Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations Applied-Matl-DOWN,Bay-Network-Down,3-COM-DOWN, Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN, DSC-Comm-DOWN,INTEL-DOWN,LSI-Logic-DOWN, Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down, Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOWN, Sun-DOWN Apple-Comp-DOWN,Autodesk-DOWN,DEC-DOWN, ADV-Micro-Device-DOWN,Andrew-Corp-DOWN, Computer-Assoc-DOWN,Circuit-City-DOWN, Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOWN,Microsoft-DOWN,Scientific-Atl-DOWN 1 2 Fannie-Mae-DOWN,Fed-Home-Loan-DOWN, MBNA-Corp-DOWN,Morgan-Stanley-DOWN 3 4 Industry Group Technology1-DOWN Technology2-DOWN Financial-DOWN Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP, Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP, Schlumberger-UP Oil-UP • Summarization – Reduce the size of large data sets Clustering precipitation in Australia Data Mining: Clustering 3 Data Mining: Clustering Clustering Examples and Applications What is not Cluster Analysis? • Segment customer database based on similar buying patterns • Group houses in a town into neighborhoods based on similar features • Group plants into categories • Identify similar Web usage patterns • Supervised classification 4 – Have class label information • Simple segmentation – Dividing students into different registration groups alphabetically, by last name • Group results of a query – When groupings are a result of an external specification • Graph partitioning Data Mining: Clustering 5 Data Mining: Clustering 6 1 Notion of a Cluster can be Ambiguous How many clusters? Six Clusters Two Clusters Four Clusters Clustering can be performed in various ways Attribute Value Based Clustering Geographic Distance Based Clustering Data Mining: Clustering 7 Data Mining: Clustering 8 Clustering Problem Clustering vs. Classification • Given a database D = {t1,t2,…,tn} of tuples and possibly also an integer value k, the clustering problem is to define a mapping f:D{1,..,k} such that each ti is assigned to one cluster Kj, 1 ≤ j ≤ k. • In classification the set of groups to which objects belong is given, and the task is to discriminate between groups on the basis of values for the attributes of the objects • A cluster, Kj, contains precisely those tuples mapped to it. • In clustering there is no prior knowledge: the meaning (and often the number) of clusters is unknown and the objective is to discover them • Clustering is sometimes known as unsupervised learning Data Mining: Clustering 9 Types of Clusterings Data Mining: Clustering 10 Partitional Clustering • A clustering is a set of clusters • Important distinction between hierarchical and partitional sets of clusters • Partitional Clustering – A division data objects into non-overlapping subsets (clusters) such that each data object is in exactly one subset • Hierarchical clustering – A set of nested clusters organized as a hierarchical tree Original Points Data Mining: Clustering 11 A Partitional Clustering Data Mining: Clustering 12 2 Hierarchical Clustering Clustering Approaches Clustering p1 p3 p4 p2 p1 p2 Traditional Hierarchical Clustering p3 p4 Traditional Dendrogram Hierarchical p1 p3 Agglomerative p4 Partitional Categorical Divisive Large DB Sampling Compression p2 p1 p2 Non-traditional Hierarchical Clustering p3 p4 Non-traditional Dendrogram Data Mining: Clustering 13 Data Mining: Clustering Other Distinctions Between Sets of Clusters Types of Clusters: Well-Separated • Exclusive vs. non-exclusive • Well-Separated Clusters: – In non-exclusive clusterings, points may belong to multiple clusters. – Can represent multiple classes or ‘border’ points • Fuzzy vs. non-fuzzy – In fuzzy clustering, a point belongs to every cluster with some weight between 0 and 1 – Weights must sum to 1 – Probabilistic clustering has similar characteristics 14 – A cluster is a set of points such that any point in a cluster is closer (or more similar) to every other point in the cluster than to any point not in the cluster. • Partial vs. complete – In some cases, we only want to cluster some of the data • Heterogeneous vs. homogeneous – Cluster of widely different sizes, shapes, and densities 3 well-separated clusters Data Mining: Clustering 15 Data Mining: Clustering Types of Clusters: Center-Based Types of Clusters: Contiguity-Based • Center-based • Contiguous Cluster (Nearest neighbor or Transitive) – A cluster is a set of objects such that an object in a cluster is closer (more similar) to the “center” of a cluster, than to the center of any other cluster – The center of a cluster is often a centroid, the average of all the points in the cluster, or a medoid, the most “representative” point of a cluster 4 center-based clusters Data Mining: Clustering 16 – A cluster is a set of points such that a point in a cluster is closer (or more similar) to one or more other points in the cluster than to any point not in the cluster. 8 contiguous clusters 17 Data Mining: Clustering 18 3 Types of Clusters: Density-Based Types of Clusters: Conceptual Clusters • Density-based • Shared Property or Conceptual Clusters – A cluster is a dense region of points, which is separated by low-density regions, from other regions of high density. – Used when the clusters are irregular or intertwined, and when noise and outliers are present. 6 density-based clusters Data Mining: Clustering – Finds clusters that share some common property or represent a particular concept. 2 Overlapping Circles 19 Data Mining: Clustering 20 Types of Clusters: Objective Function Types of Clusters: Objective Function (Cont) • Clusters Defined by an Objective Function • Map the clustering problem to a different domain and solve a related problem in that domain – Finds clusters that minimize or maximize an objective function. – Enumerate all possible ways of dividing the points into clusters and evaluate the `goodness' of each potential set of clusters by using the given objective function. (NP Hard) – Can have global or local objectives. • Hierarchical clustering algorithms typically have local objectives • Partitional algorithms typically have global objectives – A variation of the global objective function approach is to fit the data to a parameterized model. • Parameters for the model are determined from the data. • Mixture models assume that the data is a ‘mixture' of a number of statistical distributions. Data Mining: Clustering 21 Characteristics of the Data are Important – Proximity matrix defines a weighted graph, where the nodes are the points being clustered, and the weighted edges represent the proximities between points. – Clustering is equivalent to breaking the graph into connected components, one for each cluster. – Want to minimize the edge weight between clusters and maximize the edge weight within clusters. Data Mining: Clustering 22 Impact of Outliers on Clustering • Type of proximity or density measure – This is a derived measure, but central to clustering • Sparseness – Dictates type of similarity – Adds to efficiency • Attribute type – Dictates type of similarity • Type of Data – Dictates type of similarity – Other characteristics, e.g., autocorrelation • Dimensionality • Noise and Outliers • Type of Distribution Data Mining: Clustering 23 Data Mining: Clustering 24 4 Cluster Parameters Clustering Algorithms • Partitional algorithms – K-means and its variants • Hierarchical clustering algorithms • Density-based clustering algorithms Data Mining: Clustering 25 Data Mining: Clustering 26 K-means Clustering K-means Clustering - Example • Partitional clustering approach • Each cluster is associated with a centroid (center point) • Each point is assigned to the cluster with the closest centroid • Number of clusters, K, must be specified • The basic algorithm is very simple • Given a cluster Ki={ti1 ,ti2,…,tim}, let the cluster mean be mi = (1/m)(ti1 + … + tim) Given: {2,4,10,12,3,20,30,11,25}, k=2 • Randomly pick some initial means: m1=3, m2=4 • K1={2,3}, K2={4,10,12,20,30,11,25}, m1=2.5, m2=16 • K1={2,3,4}, K2={10,12,20,30,11,25}, m1=3, m2=18 • K1={2,3,4,10}, K2={12,20,30,11,25}, m1=4.75, m2=19.6 • K1={2,3,4,10,11,12}, K2={20,30,25}, m1=7, m2=25 Stop as the clusters with these means are the same. Data Mining: Clustering 27 K-means Clustering - Details Data Mining: Clustering Two different K-means Clusterings • Initial centroids are often chosen randomly. 3 Original Points 2.5 – Clusters produced vary from one run to another. 2 • The centroid is (typically) the mean of the points in the cluster. • ‘Closeness’ is measured by Euclidean distance, cosine similarity, correlation, etc. • K-means will converge for common similarity measures mentioned above. • Convergence happens in the first few iterations. • Complexity is O( n * K * I * d ) y 1.5 1 0.5 0 -2 - 1.5 -1 -0.5 0 0.5 1 1.5 2 x 3 3 2.5 2.5 2 1.5 1.5 y 2 y – Often the stopping condition is changed to ‘Until relatively few points change clusters’ 1 1 0.5 0.5 0 0 -2 – n = number of points, K = number of clusters, I = number of iterations, d = number of attributes Data Mining: Clustering 28 - 1.5 -1 -0.5 0 0.5 1 1.5 2 -2 x 29 - 1.5 -1 -0.5 0 0.5 1 1.5 2 x Optimal Clustering Sub-optimal Clustering Data Mining: Clustering 30 5 Importance of Choosing Initial Centroids Importance of Choosing Initial Centroids Iteration 1 2 Iteration 3 3 2.5 2.5 2 2 2 1.5 1.5 1.5 y 2.5 Iteration 2 3 2.5 y 3 3 y Iteration 6 1 2 3 4 5 1 1 1 0.5 0.5 0.5 0 0 1.5 -2 - 1.5 -1 -0.5 0 0.5 1 1.5 2 0 -2 - 1.5 -1 -0.5 y x 0 0.5 1 1.5 2 -2 - 1.5 -1 -0.5 x 0 0.5 1 1.5 2 1.5 2 x 1 Iteration 4 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 x 3 2.5 2 2 1.5 1.5 y 2 1.5 1 1 1 0.5 0.5 0.5 0 0 -2 - 1.5 -1 -0.5 0 0.5 1 1.5 2 0 -2 - 1.5 -1 x Data Mining: Clustering Iteration 6 3 2.5 y 0 Iteration 5 3 2.5 y 0.5 -0.5 0 0.5 1 1.5 2 -2 - 1.5 -1 -0.5 x 31 0 0.5 1 x Data Mining: Clustering Evaluating K-means Clusters 32 Importance of Choosing Initial Centroids Iteration 5 1 2 3 4 • Most common measure is Sum of Squared Error (SSE) 3 – For each point, the error is the distance to the nearest cluster – To get SSE, we square these errors and sum them. 2.5 2 K SSE = ∑ ∑ dist 2 ( mi , x ) y 1.5 i =1 x∈Ci 1 – x is a data point in cluster Ci and mi is the representative point for cluster Ci 0.5 • can show that mi corresponds to the center (mean) of the cluster 0 – Given two clusters, we can choose the one with the smallest error – One easy way to reduce SSE is to increase K, the number of clusters -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 x A good clustering with smaller K can have a lower SSE than a poor clustering with higher K Data Mining: Clustering 33 Data Mining: Clustering Importance of Choosing Initial Centroids Iteration 1 3 3 2.5 2.5 1.5 • If there are K ‘real’ clusters then the chance of selecting one centroid from each cluster is small. – Chance is relatively small when K is large – If clusters are the same size, n, then y 2 1.5 y 2 1 1 0.5 0.5 0 -2 - 1.5 -1 -0.5 0 0.5 1 1.5 2 -2 - 1.5 -1 -0.5 x 0 0.5 2.5 2.5 2.5 y 2 1.5 y 2 1.5 y 2 1.5 1 1 0.5 1 0.5 0 0.5 0 0 x 0.5 1 1.5 2 – 2 Iteration 5 3 -0.5 1.5 Iteration 4 3 -1 1 x Iteration 3 3 -1.5 Problems with Selecting Initial Points Iteration 2 0 -2 34 0 -2 -1.5 -1 -0.5 0 0.5 1 1.5 x Data Mining: Clustering 2 -2 - 1.5 -1 -0.5 0 0.5 1 1.5 2 – For example, if K = 10, then probability = 10!/1010 = 0.00036 – Sometimes the initial centroids will readjust themselves in ‘right’ way, and sometimes they don’t – Consider an example of five pairs of clusters x 35 Data Mining: Clustering 36 6 10 Clusters Example 10 Clusters Example Iteration 2 Iteration 1 Iteration 4 1 2 3 8 6 8 8 6 6 4 4 2 y y 2 4 0 -4 -4 2 -6 -6 0 5 10 15 0 20 5 10 Iteration 3 -2 -4 y 0 5 10 15 8 6 6 4 4 Data Mining: Clustering -2 -4 -4 -6 0 5 10 15 20 0 5 10 x x Starting with two initial centroids in one cluster of each pair of clusters Data Mining: Clustering 38 10 Clusters Example Ite ration 1 Iteration 4 1 2 3 Ite rat ion 2 8 8 8 6 6 4 6 4 2 0 y y 2 4 2 0 -2 -2 -4 -4 -6 -6 0 5 10 15 20 0 5 -2 y -6 8 6 6 4 4 10 15 20 -2 -4 -4 Data Mining: Clustering 10 15 20 0 5 10 x Starting with some pairs of clusters having three initial centroids, while other have only one. 39 Data Mining: Clustering Solutions to Initial Centroids Problem Pre-processing and Post-processing • Multiple runs • Pre-processing – Helps, but probability is not on your side • Sample and use hierarchical clustering to determine initial centroids • Select more than k initial centroids and then select among these initial centroids – Select most widely separated • Postprocessing • Bisecting K-means – Not as susceptible to initialization issues Data Mining: Clustering 41 20 -6 5 x Starting with some pairs of clusters having three initial centroids, while other have only one. 15 0 -2 0 x 20 2 0 -6 5 15 Ite ration 4 8 2 -4 10 x Ite ration 3 y y x 0 0 20 0 -2 37 10 Clusters Example 15 2 0 -6 20 x Starting with two initial centroids in one cluster of each pair of clusters 20 Iteration 4 8 2 -6 15 x x 0 y y 0 -2 -2 40 – Normalize the data – Eliminate outliers • Post-processing – Eliminate small clusters that may represent outliers – Split ‘loose’ clusters, i.e., clusters with relatively high SSE – Merge clusters that are ‘close’ and that have relatively low SSE – Can use these steps during the clustering process Data Mining: Clustering 42 7 Bisecting K-means Bisecting K-means Example • Bisecting K-means algorithm – Variant of K-means that can produce a partitional or a hierarchical clustering Data Mining: Clustering 43 Limitations of K-means Data Mining: Clustering 44 Limitations of K-means: Differing Sizes • K-means has problems when clusters are of differing – Sizes – Densities – Non-globular shapes • K-means has problems when the data contains outliers. Data Mining: Clustering 45 Limitations of K-means: Differing Density Data Mining: Clustering Data Mining: Clustering 46 Limitations of K-means: Non-globular Shapes Original Points K-means (3 Clusters) Original Points K-means (3 Clusters) Original Points 47 K-means (2 Clusters) Data Mining: Clustering 48 8 Overcoming K-means Limitations Original Points Overcoming K-means Limitations K-means Clusters Original Points K-means Clusters One solution is to use many clusters. Find parts of clusters, but need to put together. Data Mining: Clustering 49 Data Mining: Clustering 50 Overcoming K-means Limitations Original Points K-means Clusters Data Mining: Clustering 51 9