期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：6
页码：12568-12575
出版社：IJECS
摘要：Data Mining is knowledge discovery process in database designed to extract data from a dataset and transforms it in to desireddata. data processing action is similarly acclimated in get of constant patterns and/or analytical relationships amid variables, and a new tovalidate the accusation by applying the detected patterns to new subsets of knowledge.Data categoryification is one in every of the infomining technique to map great amount of data set in to applicable class. Data categoryification is reasonably supervised learning that isemployed to predict class for information input, wherever categories are predefined.Supervised learning is that part of automatic learningwhich focuses on modeling input/output relationship the goal of supervised learning is to identify an optimal mapping from input variablesto some output variables, which is based on a sample of observations of the values of the variables. Data classification technique includesvarious application like handwriting recognition, speech recognition, iris matching, text classification, computer vision, drug design etc.objective of this paper is to survey major techniques of data classification. Several major classification techniques are Artificial neuralnetwork, decision trees, k-nearest neighbor(KNN), support vector machine, navie-bayesian classifier, etc This paper introduces a user definekernel technique in svm for data classification, which is applicable to general data including, in particular, imagery and other types ofhigh-dimensional data. By using kernel techniques the framework can account for nonlinearity in the input space
关键词：data mining; data classification; decision tree; support vector machine; KNN; kernel
