Labeling of Web Search Result Clusters Using Heuristic Search and Frequent Itemset

Abstract

Clustering of search result is undoubtedly a tool that can provide the summarization of the millions of documents in a way where a user can easily locate his/her information. To guide user to the right cluster of documents, cluster labels should be meaningful and correctly representing the clusters. However significant a cluster is, if the label is not proper, user will never select it. In this paper, we present a method to label clusters based on their linking information. Our cluster labeling method is independent of any clustering method but restricted to only search result documents. We use heuristic search method to find all the linked documents of a cluster. If all or some documents of a cluster share hyperlinks, then we deduce label from these linked documents' titles using famous Apriori algorithm for frequent itemset mining. This removes the requirement of reviewing other members of a cluster in labeling process.

Imss-E

“…The fast development of web-based training systems is allowing the increase of the student´s autonomy, while educational applications are reaching a more diverse and heterogeneous audience. This scenario raises a renewed interest in the development of hypermedia systems that permit the automatic adaptation to different types of users, platforms and situations, which considers the evolution of each user over time (Alam, M. and Sadaf, K., 2015) [1]. A priority concern, explicitly or implicitly present in this research framed in the field of adaptive teaching is to find an adequate representation of the pedagogical knowledge (Chen, C. and Zhang, C., 2014) [2].…”