A Fuzzy Approach for Text Mining

Patil, Deepa B; Dongre, Yashwant

doi:10.5815/ijmsc.2015.04.04

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…The b e n c h m a r k datasets are taken from the UCI Repository of Machine Learning repository [15]. In Table 8 details of the five (5) datasets are shown.…”

Section: Resultsmentioning

confidence: 99%

An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

Bania¹

2018

IJMSC

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Selection of useful information from a large data collection is an important and challenging problem. Feature selection refers to the problem of selecting relevant features from a given dataset which produces the most predictive outcome as the original features maintain before the selection. Rough set theory (RST) and its extension are the most successful mathematical tools for feature selection from a given dataset. This paper starts with an outline of the fundamental concepts behind the rough set and fuzzy rough set based feature grouping techniques which are related to supervise feature selection. Supervised Quickreduct (QR) and fuzzyrough feature grouping Quickreduct (FQR) algorithms are highlighted here. Then an enhanced version of FQR method is proposed here which is based on rough set dependency criteria with feature significance measure that select a minimal subset of features. Also, the termination condition of the base method is modified. Experimental studies of the algorithms are carried out on five public domain benchmark datasets available in UCI machine learning repository. JRip and J48 classifier are used to measure the classification accuracy. The performance of the proposed method is found to be satisfactory in comparison with other methods.

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“…The b e n c h m a r k datasets are taken from the UCI Repository of Machine Learning repository [15]. In Table 8 details of the five (5) datasets are shown.…”

Section: Resultsmentioning

confidence: 99%

An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

Bania¹

2018

IJMSC

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“…The feature selection method selects the feature subset from CTM resultant matrix. The feature selection plays a vital role to speed up the process of computation as well as to improve the performance of classifier [28][29][30][31]. Finally, classifiers are used to find effectiveness of the proposed model in terms of categorization accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Text Representation Model to Categorize Text Documents using Convolution Neural Network

Revanasiddappa¹,

Harish²

2019

IJISA

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This paper presents a novel text representation model called Convolution Term Model (CTM) for effective text categorization. In the process of text categorization, representation plays a very primary role. The proposed CTM is based on Convolution Neural Network (CNN). The main advantage of proposed text representation model is that, it preserves semantic relationship and minimizes the feature extraction burden. In proposed model, initially convolution filter is applied on word embedding matrix. Since, the resultant CTM matrix is higher dimension, feature selection methods are applied to reduce the CTM feature space. Further, selected CTM features are fed into classifier to categorize text document. To discover the effectiveness of the proposed model, extensive experimentations are carried out on four standard benchmark datasets viz., 20-NewsGroups, Reuter-21758, Vehicle Wikipedia and 4 University datasets using five different classifiers. Accuracy is used to assess the performance of classifiers. The proposed model shows impressive results with all classifiers.

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“…In this method, the input objects are grouped into a smaller number of meaningful and coherent subgroups based on specified features. The text document clustering is an effective way to improve the performance of web search, search engines, and browsing techniques [3,4]. It organizes the collection of text documents into coherent groups, in such a way that the documents in each group are much the same to each other and very different to the documents in other groups.…”

Section: Introductionmentioning

confidence: 99%

“…Pearson's correlation is used to find the relationship between two variables[4]. There are different forms of Pearson's correlation.…”

mentioning

confidence: 99%

An Extensive Study of Similarity and Dissimilarity Measures Used for Text Document Clustering using K-means Algorithm

Afzali¹,

Kumar²

2018

IJITCS

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In today's world tremendous amount of unstructured data, especially text, is being generated through various sources. This massive amount of data has lead the researchers to focus on employing data mining techniques to analyse and cluster them for an efficient browsing and searching mechanisms. The clustering methods like k-means algorithm perform through measuring the relationship between the data objects. Accurate clustering is based on the similarity or dissimilarity measure that is defined to evaluate the homogeneity of the documents. A variety of measures have been proposed up to this date. However, all of them are not suitable to be used in the k-means algorithm. In this paper, an extensive study is done to compare and analyse the performance of eight well-known similarity and dissimilarity measures that are applicable to the kmeans clustering approach. For experiment purpose, four text document data sets are used and the results are reported.

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A Fuzzy Approach for Text Mining

Cited by 10 publications

References 18 publications

An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

A Novel Text Representation Model to Categorize Text Documents using Convolution Neural Network

An Extensive Study of Similarity and Dissimilarity Measures Used for Text Document Clustering using K-means Algorithm

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