Abstract: Most of the document clustering techniques are based on statistical analysis of a term, either a word or phrase.The statistical analysis of a term frequency captures the importance of the term within the document only. Thus, the underlying mining model should indicate terms that capture the semantics of the text. In this case, The mining model can capture terms that present the concepts of the sentence, which leads to the discovery of the topic of document. A new concept-based mining model focuses on the web document clustering;the model consists of three components: concept-based statistical analyzer, COG and concept extractor.The statistical analyzer is to analyze terms on the sentence and document levels. The COG is to extract the most important terms with respect to the meaning of the text. The concepts that have maximum weights are selected by the concept extractor.The similarity between documents is calculated based on the Concept-based document similarity measure;

• Publication Date: 03-Jun-2011
• DOI: 10.15224/978-981-07-1847-3-1027
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Abstract: Wireless sensor networks (WSNs) is becoming a progressively important and a challenging research area. Advancements in WSN enable a wide range of environmental monitoring and object tracking applications. Moreover, multihop routing in WSN is affected by new nodes constantly entering or leaving the network. Therefore, the self-optimized and self-aware mechanism is required to handle the problems arise very frequently in WSNs. The ant colony optimization has shown excellent results in discovering routes for WSN. In this paper, the model of self-optimized multipath routing algorithm for WSN and its results are presented. Certain parameters like energy level, delay and velocity are considered. These decisions will come up with the optimal and organized route for WSN. In addition, the stated algorithm is enhanced with the multipath capability to avoid congestion state in WSN. Eventually, the enhanced feature helps WSN in maximizing the data throughput rate and minimizing the data loss

• Publication Date: 03-Jun-2011
• DOI: 10.15224/978-981-07-1847-3-1027
• Views: 0
• Downloads: 0