Integrated Clustering and Feature Selection Scheme for Text Documents.

Thangamani, M.; Thangaraj, P.

doi:10.3844/jcssp.2010.536.541

Cited by 17 publications

(5 citation statements)

References 10 publications

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“…We have a tendency to get an accumulation of non-covering allotments exploitation this successive thing sets and along these lines the resultant bunch is produced inside the segment for the record accumulations. [7][8]…”

Section: Perceptionmentioning

confidence: 99%

Smart Learning in Document Categorization using Dynamic Learning

2019

IJRTE

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Clustering is the process of making data groups using similar data items, used for data mining to extract data from available large datasets. A large volume of text documents consisting of personal information is being generated in form of digital libraries and repositories in internet daily.It is conceivable to get to great quality instructive substance and strategies in an increasingly helpful manner. In spite of the fact that a ton of keen instruments have been connected for instructive application, there are just restricted looks into that show the instructive viability of shrewd devices through test contemplations, Clustering organizes large quantity of unordered text documents into small number of meaningful and coherent clusters. A clustering method based on K-Means algorithm is proposed in this paper. K-Means is a unsupervised algorithm based on randomly selected initial centroids used to cluster a highly unstructured and unlabeled document collection. The system will be evaluated using precision as a measure.

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Section: Perceptionmentioning

confidence: 99%

Smart Learning in Document Categorization using Dynamic Learning

2019

IJRTE

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“…al [20], proposed an intelligent agent-based weighted distance outlier-detection algorithm. While Thangamani et al [21,22,23,24,25] mooted a machine learning algorithm for semantic representation in a peer to peer environment.…”

Section: Related Workmentioning

confidence: 99%

New Scheme to Identify Intrusion Outliers by Machine Learing Technique

Thangamani¹,

Venkatesh²,

Saravanan³

2013

IJCA

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In network administration, computers and network systems need to be protected against malicious attacks. The success of an intrusion detection system depends on the selection of the appropriate features in detecting the intrusion activity. The selection of unnecessary features may cause computational issues and reduce the accuracy of detection. In the existing work, a novel detection approach is used through a one-class learning algorithm based on support vector machine classification. It can be also used in a Bayesian framework to estimate the posterior class probabilities of test data with unknown class. This algorithm can detect the system anomalies and monitor the health of a system. It does not allow updating the training data with new information. Therefore, the accuracy of the algorithm is low for the test data. The proposed work aims to improve the performance of attack detection and to reduce the false-alarm rate using hybrid classifier. This approach effectively identifies the set of attacks such as Denial of Service, Probe, and User to Root and Remote to Local attacks. In addition, an Experimental evaluation is carried out to compare the performance of existing classifier with the proposed Decision tree-Bayesian network classifier.

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“…They used a Radial Basis Function (RBF) for clustering. Another work that used semantic characteristics for text document was proposed by Raja and Narayanan (2010) and Thangamani and Thangaraj (2010). The model used a new Text Clustering with Feature Selection (TCFS) method to improve text document clustering.…”

Section: Group 3 Algorithmsmentioning

confidence: 99%

A Comparative Study To Find A Suitable Method For Text Document Clustering

Punitha¹,

Punithavalli²

2011

IJCSIT

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Text mining is used in various text related tasks such as information extraction, concept/entity extraction, document summarization, entity relation modeling (i.e., learning relations between named entities), categorization/classification and clustering. This paper focuses on document clustering, a field of text mining, which groups a set of documents into a list of meaningful categories. The main focus of this paper is to present a performance analysis of various techniques available for document clustering. The results of this comparative study can be used to improve existing text data mining frameworks and improve the way of knowledge discovery. This paper considers six clustering techniques for document clustering. The techniques are grouped into three groups namely Group 1-K-means and its variants (traditional K-means and K* Means algorithms), Group 2-Expectation Maximization and its variants (traditional EM, Spherical Gaussian EM algorithm and Linear Partitioning and Reallocation clustering (LPR) using EM algorithms), Group 3-Semantic-based techniques (Hybrid method and Feature-based algorithms). A total of seven algorithms are considered and were selected based on their popularity in the text mining field. Several experiments were conducted to analyze the performance of the algorithm and to select the winner in terms of cluster purity, clustering accuracy and speed of clustering.

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Integrated Clustering and Feature Selection Scheme for Text Documents.

Cited by 17 publications

References 10 publications

Smart Learning in Document Categorization using Dynamic Learning

Smart Learning in Document Categorization using Dynamic Learning

New Scheme to Identify Intrusion Outliers by Machine Learing Technique

A Comparative Study To Find A Suitable Method For Text Document Clustering

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