A Comparative Study of Data Mining Techniques

Abstract

Data mining is defined by different people with different manners but generally, it is nothing but a method of extracting knowledge from the data. For decades, Humans have been generating and storing large amounts of data and dumping them into the storage. Data mining can be used to extract the knowledge from this vast size of data. Different techniques are available for analysis and comparison of different available algorithms are analyzed in this paper. Among the Data mining techniques, there are different task are performed on the data based on the data and knowledge required from the acquired data. There are a variety of algorithms like Naïve Bayes, Decision Tree-based Algorithms, Rule-based classifiers, etc. Data mining techniques are divided into several methods like Classification, Regression, Clustering, Association Rules, etc. In this comparative study, different algorithms available in some of the mentioned categories will be compared based on the accuracy and time complexity of the algorithms.