Article: Data classification methodology and pattern analysis for knowledge discovery and data mining Journal: International Journal of Digital Enterprise Technology (IJDET) 2022 Vol.2 No.1 pp.15 - 26 Abstract: Excess usage of information with the help of big data (BD) applications, the society of computer sciences needs to adopt various online classification tools and different types of pattern reorganisation (PR) techniques. The data collected from different types of sensor networks (SN), online patterns, images, etc. are the different sources of big data and to deal with such data, natural processing (NP) and data mining (DM) techniques are not capable of facing the challenges to deal with huge amount of data. To deal with such type of challenges, in this research a new algorithm is proposed based on DM and knowledge discovery (KD) using the network entropy (NE). Initially, the needful data is introduced using support vector machine (SVM), neural networks (NN) and decision trees (DT) methods. In the later stages, using machine learning (ML) techniques and pattern theory an organisational structure of network graphical pattern is constructed. From different databases, the simulation results obtained from the proposed approach shows good effectiveness and feasibility. Inderscience Publishers - linking academia, business and industry through research

Title: Data classification methodology and pattern analysis for knowledge discovery and data mining

Authors: Attili Venkata Ramana; Annaluri Sreenivasa Rao; Somula Ramasubbareddy

Addresses: Department of Electronics and Computer Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana 501301, India ' Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana 500090, India ' Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana 500090, India

Abstract: Excess usage of information with the help of big data (BD) applications, the society of computer sciences needs to adopt various online classification tools and different types of pattern reorganisation (PR) techniques. The data collected from different types of sensor networks (SN), online patterns, images, etc. are the different sources of big data and to deal with such data, natural processing (NP) and data mining (DM) techniques are not capable of facing the challenges to deal with huge amount of data. To deal with such type of challenges, in this research a new algorithm is proposed based on DM and knowledge discovery (KD) using the network entropy (NE). Initially, the needful data is introduced using support vector machine (SVM), neural networks (NN) and decision trees (DT) methods. In the later stages, using machine learning (ML) techniques and pattern theory an organisational structure of network graphical pattern is constructed. From different databases, the simulation results obtained from the proposed approach shows good effectiveness and feasibility.

Keywords: data classification; PA; knowledge discovery; KD; information security; PA and BD.

DOI: 10.1504/IJDET.2022.124983

International Journal of Digital Enterprise Technology, 2022 Vol.2 No.1, pp.15 - 26

Received: 08 Aug 2019
Accepted: 15 Oct 2019
Published online: 22 Aug 2022 *

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Title: Data classification methodology and pattern analysis for knowledge discovery and data mining

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