Comparative Survey Of Different Classification Techniques In Data Mining

Authors

  • Asst. Prof. Nishant Sanghani Computer Science & Engineering, SLTIET
  • Asst. Prof. Pooja Vasani Computer Science & Engineering, AITS
  • Asst. Prof. Ravi Khimani Computer Science & Engineering, SLTIET

Keywords:

Bayesian, classification technique, fuzzy logic, ID3, k-nearest neighbour, Decision Tree induction

Abstract

Classification is a data mining (machine learning) technique used to predict group membership for data
instances. Classification is used to find out in which group each data instance is related within a given dataset. It is
used for classifying data into different classes according to some constrains. Several major kinds of classification
algorithms / techniques including decision tree induction, Bayesian networks, k-nearest neighbor classifier, casebased reasoning, genetic algorithm, C4.5, ID3 and fuzzy logic techniques. The goal of this survey is to provide a
comprehensive review of different classification techniques in data mining.

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Published

2016-12-31

How to Cite

Asst. Prof. Nishant Sanghani, Asst. Prof. Pooja Vasani, & Asst. Prof. Ravi Khimani. (2016). Comparative Survey Of Different Classification Techniques In Data Mining. International Journal of Advance Research in Engineering, Science & Technology, 3(13), -. Retrieved from https://ijarest.org/index.php/ijarest/article/view/2162

Issue

Vol. 3 No. 13 (2016): International Conference On "Innovative Trends In Electrical And Computer Engineering (ITECE-2016) Organized by Shri Labhubhai Trivedi Institute of Engineering & Technology, Rajkot

Section

Articles