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Document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of documents into a small number of meaningful clusters. Most of the documents clustering methods were grounded in the bag of words representation to measure similarity, ignoring the semantic relationships between words that do not co-occur literally. A novel fuzzy semantic...
Load balance algorithms are important in the research of network applications. In nowadays enterprise applications, due to the rapidly changed requirement and being lack of interactive, low level of integration, so this article proposed a queue theory based algorithm aiming at the flaws. This load balance algorithm based on queue theory, according to the current load situation and the rest processing...
In this paper, a new method for enhancing the performance of Neural Network ensemble is proposed. The main idea of this method is creating diversity for training artificial neural networks (ANNs) using an interesting method which applies clustering ensemble and genetic algorithm. In combinational classifier systems, the more diversity in results of the base classifiers yields to better final performance...
This paper proposes a new query based personalized summarization agent using non-negative matrix factorization (NMF) and relevance feedback (RF) to extract meaningful sentences from to retrieve documents in Internet. The proposed method can improve the quality of personalized summaries because the inherent semantics of the documents are well reflected by using the semantic features calculated by NMF...
Clustering is an effective way to organize Web search results, which allows users to navigate into relevant documents quickly. Traditional clustering techniques are inadequate to Chinese search results and do not generated clusters with highly readable names. In this paper, we propose a new method to clustering Web search results which is based on interactive suffix tree algorithm (ISTC). This method...
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