Data Mining: The Textbook
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Point selection: Different subsets of the data are selected, either via random sampling, or by systematic selection for the clustering process.
Dimension selection: Different subsets of dimensions are selected to perform the clus-tering. An example is the IPCLUS method discussed in the previous section. After the individual ensemble components have been constructed, it is often a challenge to combine the results from these different components to create a consensus clustering. 7.7.2 Combining Different Ensemble Components After the different clustering solutions have been obtained, it is desired to create a robust consensus from the different solutions. In the following section, some simple methods are described that use the base clusterings as input for the generation of the final set of clusters. 7.7.2.1 Hypergraph Partitioning Algorithm Each object in the data is represented by a vertex. A cluster in any of the ensemble compo-nents is represented as a hyperedge. A hyperedge is a generalization of the notion of edge, because it connects more than two nodes in the form of a complete clique. Any off-the-shelf hypergraph clustering algorithm such as HMETIS [302] can be used to determine the optimal partitioning. Constraints are added to ensure a balanced partitioning. One major challenge with hypergraph partitioning is that a hyperedge can be “broken” by a partition-ing in many different ways, not all of which are qualitatively equivalent. Most hypergraph partitioning algorithms use a constant penalty for breaking a hyperedge. This can sometimes be undesirable from a qualitative perspective. 7.7.2.2 Meta-clustering Algorithm This is also a graph-based approach, except that vertices are associated with each cluster in the ensemble components. For example, if there are k1 . . . kr different clusters in each of the r ensemble components, then a total of r ki vertices will be created. Each vertex therefore i=1 represents a set of data objects. An edge is added between a pair of vertices if the Jaccard coefficient between the corresponding object sets is nonzero. The weight of the edge is equal to the Jaccard coefficient. This is therefore an r-partite graph because there are no edges
between two vertices from the same ensemble component. A graph partitioning algorithm is applied to this graph to create the desired number of clusters. Each data point has r different instances corresponding to the different ensemble components. The distribution of the membership of different instances of the data point to the meta-partitions can be used to determine its meta-cluster membership, or soft assignment probability. Balancing constraints may be added to the meta-clustering phase to ensure that the resulting clusters are balanced. 7.8 Putting Clustering to Work: Applications Clustering can be considered a specific type of data summarization where the summaries of the data points are constructed on the basis of similarity. Because summarization is a first step to many data mining applications, such summaries can be widely useful. This section will discuss the many applications of data clustering. 7.8.1 Applications to Other Data Mining Problems Clustering is intimately related to other data mining problems and is used as a first summa-rization step in these cases. In particular, it is used quite often for the data mining problems of outlier analysis and classification. These specific applications are discussed below. 7.8.1.1 Data Summarization Although many forms of data summarization, such as sampling, histograms, and wavelets are available for different kinds of data, clustering is the only natural form of summariza-tion based on the notion of similarity. Because the notion of similarity is fundamental to many data mining applications, such summaries are very useful for similarity-based applica-tions. Specific applications include recommendation analysis methods, such as collaborative filtering. This application is discussed later in this chapter, and in Chap. 18 on Web mining. 7.8.1.2 Outlier Analysis Outliers are defined as data points that are generated by a different mechanism than the normal data points. This can be viewed as a complementary problem to clustering where the goal is to determine groups of closely related data points generated by the same mechanism. Therefore, outliers may be defined as data points that do not lie in any particular cluster. This is of course a simplistic abstraction but is nevertheless a powerful principle as a starting point. Sections 8.3 and 8.4 of Chap. 8 discuss how many algorithms for outlier analysis can be viewed as variations of clustering algorithms. 7.8.1.3 Classification Many forms of clustering are used to improve the accuracy of classification methods. For example, nearest-neighbor classifiers report the class label of the closest set of training data points to a given test instance. Clustering can help speed up this process by replacing the data points with centroids of fine-grained clusters belonging to a particular class. In addition, semisupervised methods can also be used to perform categorization in many domains such as text. The bibliographic notes contain pointers to these methods. 234 CHAPTER 7. CLUSTER ANALYSIS: ADVANCED CONCEPTS 7.8.1.4 Dimensionality Reduction Clustering methods, such as nonnegative matrix factorization, are related to the problem of dimensionality reduction. In fact, the dual output of this algorithm is a set of concepts, together with a set of clusters. Another related approach is probabilistic latent semantic indexing, which is discussed in Chap. 13 on mining text data. These methods show the intimate relationship between clustering and dimensionality reduction and that common solutions can be exploited by both problems. 7.8.1.5 Similarity Search and Indexing A hierarchical clustering such as CF-Tree can sometimes be used as an index, at least from a heuristic perspective. For any given target record, only the branches of the tree that are closest to the relevant clusters are searched, and the most relevant data points are returned. This can be useful in many scenarios where it is not practical to build exact indexes with guaranteed accuracy. 7.8.2 Customer Segmentation and Collaborative Filtering In customer segmentation applications, similar customers are grouped together on the basis of the similarity of their profiles or other actions at a given site. Such segmentation methods are very useful in cases where the data analysis is naturally focused on similar segments of the data. A specific example is the case of collaborative filtering applications in which ratings are provided by different customers based on their items of interest. Similar customers are grouped together, and recommendations are made to the customers in a cluster on the basis of the distribution of ratings in a particular group. 7.8.3 Text Applications Many Web portals need to organize the material at their Web sites on the basis of simi-larity in content. Text clustering methods can be useful for organization and browsing of text documents. Hierarchical clustering methods can be used to organize the documents in an exploration-friendly tree structure. Many hierarchical directories in Web sites are con-structed with a combination of user labeling and semisupervised clustering methods. The semantic insights provided by hierarchical cluster organizations are very useful in many applications. 7.8.4 Multimedia Applications With the increasing proliferation of electronic forms of multimedia data, such as images, photos, and music, numerous methods have been designed in the literature for finding clusters in such scenarios. Clusters of such multimedia data also provide the user the ability to search for relevant objects in social media Web sites containing this kind of data. This is because heuristic indexes can be constructed with the use of clustering methods. Such indexes are useful for effective retrieval. 7.8.5 Temporal and Sequence Applications Many forms of temporal data, such as time-series data, and Web logs can be clustered for effective analysis. For example, clusters of sequences in a Web log provide insights
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