Cluster hypothesis. Documents in the same cluster behave similarly with respect to relevance to information needs.The hypothesis states that if there is a document from a cluster that is relevant to a search request, then it is likely that other documents from the same cluster are also relevant. This is because clustering puts together documents that share many terms. The cluster hypothesis essentially is the contiguity hypothesis in Chapter 14 (page 14 ). In both cases, we posit that similar documents behave similarly with respect to relevance.
Table 16.1 shows some of the main applications of clustering in information retrieval. They differ in the set of documents that they cluster - search results, collection or subsets of the collection - and the aspect of an information retrieval system they try to improve - user experience, user interface, effectiveness or efficiency of the search system. But they are all based on the basic assumption stated by the cluster hypothesis.
The first application mentioned in Table 16.1 is search result clustering where by search results we mean the documents that were returned in response to a query. The default presentation of search results in information retrieval is a simple list. Users scan the list from top to bottom until they have found the information they are looking for. Instead, search result clustering clusters the search results, so that similar documents appear together. It is often easier to scan a few coherent groups than many individual documents. This is particularly useful if a search term has different word senses. The example in Figure 16.2 is jaguar. Three frequent senses on the web refer to the car, the animal and an Apple operating system. The Clustered Results panel returned by the Vivísimo search engine (http://vivisimo.com) can be a more effective user interface for understanding what is in the search results than a simple list of documents.
A better user interface is also the goal of Scatter-Gather , the second application in Table 16.1 . Scatter-Gather clusters the whole collection to get groups of documents that the user can select or gather. The selected groups are merged and the resulting set is again clustered. This process is repeated until a cluster of interest is found. An example is shown in Figure 16.3 .
Automatically generated clusters like those in Figure 16.3 are not as neatly organized as a manually constructed hierarchical tree like the Open Directory at http://dmoz.org. Also, finding descriptive labels for clusters automatically is a difficult problem (Section 17.7 , page 17.7 ). But cluster-based navigation is an interesting alternative to keyword searching, the standard information retrieval paradigm. This is especially true in scenarios where users prefer browsing over searching because they are unsure about which search terms to use.
As an alternative to the user-mediated iterative clustering in Scatter-Gather, we can also compute a static hierarchical clustering of a collection that is not influenced by user interactions (``Collection clustering'' in Table 16.1 ). Google News and its precursor, the Columbia NewsBlaster system, are examples of this approach. In the case of news, we need to frequently recompute the clustering to make sure that users can access the latest breaking stories. Clustering is well suited for access to a collection of news stories since news reading is not really search, but rather a process of selecting a subset of stories about recent events.
The fourth application of clustering exploits the cluster hypothesis directly for improving search results, based on a clustering of the entire collection. We use a standard inverted index to identify an initial set of documents that match the query, but we then add other documents from the same clusters even if they have low similarity to the query. For example, if the query is car and several car documents are taken from a cluster of automobile documents, then we can add documents from this cluster that use terms other than car (automobile, vehicle etc). This can increase recall since a group of documents with high mutual similarity is often relevant as a whole.
More recently this idea has been used for language modeling. Equation 102 , page 102 , showed that to avoid sparse data problems in the language modeling approach to IR, the model of document can be interpolated with a collection model. But the collection contains many documents with terms untypical of . By replacing the collection model with a model derived from 's cluster, we get more accurate estimates of the occurrence probabilities of terms in .
Clustering can also speed up search. As we saw in Section 6.3.2 ( page 6.3.2 ) search in the vector space model amounts to finding the nearest neighbors to the query. The inverted index supports fast nearest-neighbor search for the standard IR setting. However, sometimes we may not be able to use an inverted index efficiently, e.g., in latent semantic indexing (Chapter 18 ). In such cases, we could compute the similarity of the query to every document, but this is slow. The cluster hypothesis offers an alternative: Find the clusters that are closest to the query and only consider documents from these clusters. Within this much smaller set, we can compute similarities exhaustively and rank documents in the usual way. Since there are many fewer clusters than documents, finding the closest cluster is fast; and since the documents matching a query are all similar to each other, they tend to be in the same clusters. While this algorithm is inexact, the expected decrease in search quality is small. This is essentially the application of clustering that was covered in Section 7.1.6 (page 7.1.6 ).