Search Result Clustering via Randomized Partitioning of Query-Induced Subgraphs

In this paper, we present an approach to search result clustering, using partitioning of underlying link graph. We define the notion of "query-induced subgraph" and formulate the problem of search result clustering as a problem of efficient partitioning of given subgraph into topic-related clusters. Also, we propose a novel algorithm for approximative partitioning of such graph, which results in cluster quality comparable to the one obtained by deterministic algorithms, while operating in more efficient computation time, suitable for practical implementations. Finally, we present a practical clustering search engine developed as a part of this research and use it to get results about real-world performance of proposed concepts.Comment: 16th Telecommunications Forum TELFOR 200

Explicit probabilistic models for databases and networks

Recent work in data mining and related areas has highlighted the importance of the statistical assessment of data mining results. Crucial to this endeavour is the choice of a non-trivial null model for the data, to which the found patterns can be contrasted. The most influential null models proposed so far are defined in terms of invariants of the null distribution. Such null models can be used by computation intensive randomization approaches in estimating the statistical significance of data mining results. Here, we introduce a methodology to construct non-trivial probabilistic models based on the maximum entropy (MaxEnt) principle. We show how MaxEnt models allow for the natural incorporation of prior information. Furthermore, they satisfy a number of desirable properties of previously introduced randomization approaches. Lastly, they also have the benefit that they can be represented explicitly. We argue that our approach can be used for a variety of data types. However, for concreteness, we have chosen to demonstrate it in particular for databases and networks.Comment: Submitte