Appropriate clustering of objects into pages in secondary memory is crucial to achieving good performance in a persistent object store. We present a new approach, termed semantic clustering, that exploits more of a program’s data accessing semantics than previous proposals. We insulate the source code from changes in clustering, so that clustering only impacts performance. The linguistic constr...

Document clustering, one of the traditional data mining techniques, is an unsupervised learning paradigm where clustering methods try to identify inherent groupings of the text documents, so that a set of clusters is produced in which clusters exhibit high intra-cluster similarity and low intercluster similarity. The importance of document clustering emerges from the massive volumes of textual ...

In this thesis proposal we present a novel semantic embedding method, which aims at consistently performing semantic clustering at sentence level. Taking into account special aspects of Vector Space Models (VSMs), we propose to learn reproducing kernels in classification tasks. By this way, capturing spectral features from data is possible. These features make it theoretically plausible to mode...

Problem statement: Text documents are the unstructured databases that contain raw data collection. The clustering techniques are used group up the text documents with reference to its similarity. Approach: The feature selection techniques were used to improve the efficiency and accuracy of clustering process. The feature selection was done by eliminate the redundant and irrelevant items from th...

In this paper, we propose a method of MultiObjective Genetic Algorithms (MOGAs), NSGA-II and SPEA2, for document clustering with semantic similarity measures based on WordNet. The MOGAs showed a high performance compared to other clustering algorithms. The main problem in the application of MOGAs for document clustering in the Vector Space Model (VSM) is that it ignores relationships between im...

Semantic relation clustering for unsupervised information extraction Most studies in unsupervised information extraction concentrate on the relation extraction and few work has been proposed on the organization of the extracted relations. We present in this paper a two-step clustering procedure to group semantically equivalent relations : a first step clusters relations with similar expressions...

In this paper, we present a tool for the semantic categorization and clustering of opendomain named entities (NEs) and common nouns (CNs), the Categorization And Clustering Tool for User-defined Semantic classes, or CACTUS. The tool performs either of the two tasks, using Hearst-style (Hearst, 1992) search queries with a web search engine: assignment of NEs/CNs to user-provided semantic classes...