A NEW HYBRID MODEL OF K-MEANS AND NAÏVE BAYES ALGORITHMS FOR FEATURE SELECTION IN TEXT DOCUMENTS CATEGORIZATION

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ALLAHVERDIPOOR ALI; SOLEIMANIAN GHAREHCHOPOGH FARHAD

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Paper Information

Journal: JOURNAL OF ADVANCES IN COMPUTER RESEARCH Year:2017 | Volume:8 | Issue:4 (30) Page(s): 73-86

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Title

A NEW HYBRID MODEL OF K-MEANS AND NAÏVE BAYES ALGORITHMS FOR FEATURE SELECTION IN TEXT DOCUMENTS CATEGORIZATION

Author(s)

ALLAHVERDIPOOR ALI | SOLEIMANIAN GHAREHCHOPOGH FARHAD | Issue Writer Certificate

Keywords

TEXT CATEGORIZATION

MACHINE LEARNING

FEATURE SELECTION

K-MEANS ALGORITHM

NAÏVE BAYES ALGORITHM

Abstract

With increasing speed of information and documents on the Web, our need to classify them in different categories and clusters is more necessary. Clustering tries to find related structures in data sets which they are not categorized, yet. Concerning the needs, a new approach for text documents categorization is presented in this paper which includes three phases: pre-processing documents and selection feature, K-Means clustering and Naïve Bayes (NB) optimization. The proposed model uses K-Means and NB algorithms that utilize K-MEANS ALGORITHM to find minimum distances between features from the center of clusters and NB algorithm for computing the probability of each feature into documents and using them to cluster features, separately. The proposed model optimizes performance of K-MEANS ALGORITHM by using NB properties in clustering. Therefore, the model overcomes to the challenges of labeling different documents and origin of K-MEANS ALGORITHM which it refers to categorizing text documents as un-supervised model. Finally, the experiment results of proposed model and K-MEANS ALGORITHMs are evaluated based on evaluation methods and are compared in validated datasets.

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APA: Copy

ALLAHVERDIPOOR, ALI, & SOLEIMANIAN GHAREHCHOPOGH, FARHAD. (2017). A NEW HYBRID MODEL OF K-MEANS AND NAIVE BAYES ALGORITHMS FOR FEATURE SELECTION IN TEXT DOCUMENTS CATEGORIZATION. JOURNAL OF ADVANCES IN COMPUTER RESEARCH, 8(4 (30)), 73-86. SID. https://sid.ir/paper/328896/en

Vancouver: Copy

ALLAHVERDIPOOR ALI, SOLEIMANIAN GHAREHCHOPOGH FARHAD. A NEW HYBRID MODEL OF K-MEANS AND NAIVE BAYES ALGORITHMS FOR FEATURE SELECTION IN TEXT DOCUMENTS CATEGORIZATION. JOURNAL OF ADVANCES IN COMPUTER RESEARCH[Internet]. 2017;8(4 (30)):73-86. Available from: https://sid.ir/paper/328896/en

IEEE: Copy

ALI ALLAHVERDIPOOR, and FARHAD SOLEIMANIAN GHAREHCHOPOGH, “A NEW HYBRID MODEL OF K-MEANS AND NAIVE BAYES ALGORITHMS FOR FEATURE SELECTION IN TEXT DOCUMENTS CATEGORIZATION,” JOURNAL OF ADVANCES IN COMPUTER RESEARCH, vol. 8, no. 4 (30), pp. 73–86, 2017, [Online]. Available: https://sid.ir/paper/328896/en

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