“Comparison the various Classification and Clustering Algorithms using WEKA Tool on data set”

COMPUTER SCIENCE

With the information technology advancement, huge amount of data is available around each corner. To extract meaningful information and hidden patterns from available data, data mining techniques can be applied. There are many techniques in data mining to find hidden patterns such as clustering, classification, and association rule mining and regression analysis. Data mining is the process is to extract information from a data set and transform it into an understandable structure. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Clustering is dividing data set into related groups such that all the groups do not have anything in common. Prediction, as the name suggests, predictions are made with available data set. It does not give surety of any kind, it may predict right or may predict wrong. Classification is classification of data sets into some predefined sets using various mathematical models. Association is discovering a correlation hidden in large amount of data, that is, in a given transaction based on the relationships between the items a pattern is discovered. In this research, we study one of the most widely used methods to handle big data, that is, data mining clustering algorithms and classification algorithms. Here we have studied and made a comparative analysis of classification and clustering algorithms. A comparison between proposed study and earlier method is performed in terms of certain parameters such as accuracy, error recognition rate and execution time.

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