August 2017 Issue Vol.7 No.8
Assistant Professor, PG and Research Department of Computer Science,Kaamadhenu Arts and Science College, Sathyamangalam, Tamil Nadu, India
S.Subasri & T.Priyanka
M.Phil Research Scholar, PG and Research Department of Computer Science,Kaamadhenu Arts and Science College, Sathyamangalam, Tamil Nadu, India
Abstract: WordNet is connected to several databases of the semantic web. WordNet is also commonly re-used via mapping between the WordNet synsets and the categories from ontologies. Most often, only the top-level categories of wordnet are mapped. It is used for a number of different purpose in information systems, including word-sense disambiguation information retrieval, automatic text classification, automatic text summarization, machine translation and even automatic crossword puzzle generation. Mostly this information data is stored in unstructured text. This large data developed has lead to the need of its systematic clustering for easy data retrieval organization and summarization, typically called as data mining. In this paper we Present document clustering using wordnet used different attributes and algorithm. Wordnet based algorithm is used for semantic similarity measure. It is designed to solve problems in text clustering. Semantic algorithm is compared with using all algorithm, Which proved to be more efficient and provides more pure clusters.
Keywords:Suffix Tree, Lingo, Suffix Array, Information Retrieval, Search Engine, Semantic, Tree Clustering,Document Clustering.
M.Phil Research Scholar, Department of Computer Science,Vellalar College for Women(Autonomous),Erode, Tamil Nadu, India
Assistant Professor, Department of Computer Science,Vellalar College for Women(Autonomous),Erode, Tamil Nadu, India
Abstract: Wireless sensor network plays an important role in monitoring environmental activities. Many sensor devices are used to collect the spatial or temporal data. The data sets that are collected may have irregularities, missing values inconsistent data. To handle these data, data preprocessing is performed to remove, unwanted data and to fill in the missing values .Various clustering algorithm is performed on those data for cluster formation. This project analyses the two major clustering algorithms: K-means clustering and Fuzzy C-means clustering .The clusters are formed using both the algorithms and their performance is analyzed .The performance of these clusters are analyzed based on the inter and intra cluster distance. Based on the result, it is proved that the Fuzzy C means algorithm is efficient than K-means algorithm.
Keywords:Wireless sensor, clustering, data preprocessing, Fuzzy C, K-means algorithm.