International Journal of Research & Review (www.ijrrjournal.com) 214 Vol.5; Issue: 10; October 2018 International Journal of Research and Review www.ijrrjournal.com E-ISSN: 2349-9788; P-ISSN: 2454-2237 Research Paper 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. Keywords: data classification, support vector machine, k-nearest neighbor, fuzzy INTRODUCTION Data classifications is broadly defined as a process of gathering, sorting and categorizing acquired data into relevant types, forms or any distinct class. Once the data have been classified, it may be used and protected more efficiently. The classification process of data will not only make the data to be easier to retrieved, but also easier to be located. Data classification is importance when we are dealing with data security, compliance and risk management. Normally, any classification of data occurs in daily human activity. The data classification procedure for true classes has variously termed pattern recognition, discrimination, or supervised learning. Most of problem in daily life can be related as classification or decision using complex data. [1] In this paper, we are focusing on three techniques which are Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Fuzzy. SUPPORT VECTOR MACHINES (SVM) Support vector machines are one of the methods of data classification with learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. [2] Lin et al. [3] proposed a simple procedure which usually gives reasonable results. A classification task usually involves separating data into training and testing sets. Each instance in the training set contains one “target value” and several “attributes”. The goal of SVM is to produce a model (based on the training data) which predicts the target values of the test data given only the test data attributes. The goal of SVM is to produce a model (based on the training data) which predicts the target values of the test data given only the test data attributes. [3] Schölkopf [2] stated that Support- vector learning is very useful in two respects. First, it is quite satisfying from a theoretical point of view. SV learning is