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Clustering is one of the prime topics in data mining. Clustering partitions the data and classifies the data into meaningful subgroups. Document clustering is a set of the document into groups such that two groups show different characteristics with respect to likeness. In this paper, an experimental exploration of similarity based method, HSC for measuring the similarity between data objects particularly...
Social network search is a kind of vertical search based on the information polymerization of social network data. And name disambiguation is one of the vital issues in social network search. With the explosive growth of the information, effectively deal with name disambiguation in massive data scenario becomes an important issue. To tackle this issue, we combine the MapReduce model and document clustering...
This paper presents a method to improve the performance of Information Retrieval System (IRS) by increasing the no of relevant documents retrieved. There are several types of uncertainty and fuzziness associated with IRS like search term uncertainty, relevance uncertainty involved in retrieving of irrelevant documents. The aim of this paper is to eliminate different types of uncertainty and increase...
With the advent of Google Earth, it is an interesting idea that we build a Web-3D virtual trading community where users can have their own houses or offices and engage in trading activities with each other. Thus, it is necessary to develop an intelligent project agency (IPA) to deal with the huge amount of project information. IPA is an effective way of not only filtering and clustering the users'...
In this paper, an method based on formal concept analysis for explaining the document clustering result is presented. In order to decrease the number of objects in formal concept analysis, the documents is clustered by high dimensional sparse clustering approach. The clusters are considered as the objects of formal context; The attributes set is the characters set of centroid vectors. The concept...
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