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Learn about text clustering, its importance in organizing documents, and the kMeans algorithm for effective clustering. Discover how clustering enhances user interface, navigation, recall, and retrieval speed.
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Information Retrieval For the MSc Computer Science Programme Lecture 6 Introduction to Information Retrieval (Manning et al. 2007) Chapter 16 Dell Zhang Birkbeck, University of London
What is text clustering? • Text clustering – grouping a set of documents into classes of similar documents. • Classification vs. Clustering • Classification: supervised learning • Labeled data are given for training • Clustering: unsupervised learning • Only unlabeled data are available
Why text clustering? • To improve user interface • Navigation/analysisof corpus or search results • To improve recall • Cluster docs in corpus a priori. When a query matches a doc d, also return other docs in the cluster containing d. Hope if we do this,the query “car” will also return docs containing “automobile”. • To improve retrieval speed • Cluster Pruning
Whatclustering is good? • External criteria • Consistent with the latent classes in gold standard(ground truth) data. • Internal criteria • High intra-cluster similarity • Low inter-cluster similarity
Issues for Clustering • Similarity between docs • Ideal: semantic similarity • Practical: statistical similarity, e.g., cosine. • Number of clusters • Fixed, e.g., kMeans. • Flexible, e.g., Single-Link HAC. • Structure of clusters • Flat partition, e.g., kMeans. • Hierarchical tree, e.g., Single-Link HAC.
kMeans Algorithm • Pickk docs {s1, s2,…,sk}randomly as seeds. • Repeat until clustering converges • (or other stopping criterion): • For each doc di : • Assign di to cluster cjsuch that sim(di, sj) is maximal. • For each cluster cj : • Update sj to the centroid(mean) of cluster cj.
Pick seeds Reassign clusters Compute centroids Reassign clusters x x Compute centroids x x x x kMeans – Example (k = 2) Reassign clusters Converged!
kMeans – Online Demo • http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html
Convergence • kMeans is proved to converge, i.e., to reach a state in which clusters don’t change. • kMeans usually converges quickly, i.e., the number of iterations is small in most cases.
Problem Results can vary because of random seed selections. Some seeds can result in poor convergence rate, or convergence to sub-optimal clustering. Solution Try kMeans for multiple times with different random seed selections. …… Seeds Example showing sensitivity to seeds In the above, if you start with B and E as centroids you converge to {A,B,C} and {D,E,F} If you start with D and F you converge to {A,B,D,E} {C,F}
Take Home Message • kMeans
