August 2017 Issue Vol.7 No.8
A Survey on Document Clustering Using
Wordnet
https://archive.org/download//vol7no0801/vol7no0801.pdf
M.Sangeetha
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.
Wireless Sensor Network using
Monitoring the Environmental Activities
https://archive.org/download//vol7no0802/vol7no0802.pdf
S.Kalaivani
M.Phil Research Scholar, Department of Computer Science,Vellalar College for Women(Autonomous),Erode, Tamil Nadu, India
Dr.P.Radha
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.
Read complete August 2017
S.Kalaivani
M.Phil Research Scholar, Department of Computer Science,Vellalar College for Women(Autonomous),Erode, Tamil Nadu, India
Dr.P.Radha
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.