Research Paper
Year: 2018 | Month: October | Volume: 5 | Issue: 10 | Pages: 214-221
Literature Survey on Data Classification Techniques
S.S.N. Alhady, M.A.N. Mohammad, A.A.A. Wahab, W.A.F.W. Othman
School of Electrical & Electronic Engineering, Universiti Sains Malaysia 14300 Nibong Tebal, Penang, Malaysia.
Corresponding Author: W.A.F.W. Othman
ABSTRACT
The growth of computer systems makes it easier to extract information from swarm of multiple nodes. These extracted big data will then be classified and divided into several categories for analysis. There are many ways to classify data, and this paper focuses on three, i.e. Support Vector Machine (SVM), K-Nearest Neighbor (kNN) and Fuzzy. The paper briefly explains each type of classifications and brings some of the physical systems that are using the data classification technique found on literatures. The goal is to summarize the existing approach towards data classification, guides the creation of new systems and point towards future directions.
Key words: data classification, support vector machine, k-nearest neighbor, fuzzy
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