An Efficient Concept-Based Mining Model for Enhancing Text Clustering

2010 ◽  
Vol 22 (10) ◽  
pp. 1360-1371 ◽  
Author(s):  
Shady Shehata ◽  
Fakhri Karray ◽  
Mohamed Kamel
Keyword(s):  
Text Clustering ◽  
Concept Based Mining ◽  
Mining Model

scholarly journals TEXT CLUSTERING IN CONCEPT BASED MINING

2016 ◽  
pp. 32-34
Author(s):  
PRADNYA S. RANDIVE ◽  
NITIN N. PISE
Keyword(s):  
Statistical Analysis ◽  
Text Mining ◽  
Similarity Measure ◽  
Text Clustering ◽  
Concept Analysis ◽  
Experimental Result ◽  
Combined Approach ◽  
Clustering Quality ◽  
Concept Based Mining ◽  
Mining Model

In text mining most techniques depends on statistical analysis of terms. Statistical analysis trances important terms within document only. However this concept based mining model analyses terms in sentence, document and corpus level. This mining model consist of sentence based concept analysis, document based and corpus based concept analysis and concept based similarity measure. Experimental result enhances text clustering quality by using sentence, document, corpus and combined approach of concept analysis.

Concept-Based Mining Model

2010 ◽  
pp. 57-69 ◽  
Author(s):  
Shady Shehata ◽  
Fakhri Karray ◽  
Mohamed Kamel
Keyword(s):  
Statistical Analysis ◽  
Text Mining ◽  
Text Clustering ◽  
The Other ◽  
Graph Representation ◽  
Sentence Meaning ◽  
Proposed Model ◽  
Concept Based Mining ◽  
Mining Model

Most of text mining techniques are based on word and/or phrase analysis of the text. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that present the concepts of the sentence, which leads to discover the topic of the document. A new concept-based mining model that relies on the analysis of both the sentence and the document, rather than, the traditional analysis of the document dataset only is introduced. The concept-based model can effectively discriminate between non-important terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed model consists of concept-based statistical analyzer, conceptual ontological graph representation, and concept extractor. The term which contributes to the sentence semantics is assigned two different weights by the concept-based statistical analyzer and the conceptual ontological graph representation. These two weights are combined into a new weight. The concepts that have maximum combined weights are selected by the concept extractor. The concept-based model is used to enhance the quality of the text clustering, categorization and retrieval significantly.

An Efficient Concept Based Mining Model for Web Page Clustering

2015 ◽  
Vol 21 (4) ◽  
pp. 219-221
Author(s):  
Mr. P. S Gamare ◽  
◽  
Mr. Sandip B. Khedkar ◽  
Mr. Maheshwar A. Panindre ◽  
Mr. Ketan D. Bhatkar
Keyword(s):  
Web Page ◽  
Concept Based Mining ◽  
Web Page Clustering ◽  
Mining Model

K-means text clustering algorithm based on density and nearest neighbor

2010 ◽  
Vol 30 (7) ◽  
pp. 1933-1935 ◽  
Author(s):  
Wen-ming ZHANG ◽  
Jiang WU ◽  
Xiao-jiao YUAN
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Text Clustering
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