Comparative Analysis of Bayes and Lazy Classification Algorithms

Data mining is the non-trivial extraction of implicit, earlier unknown and potentially useful information about data. There are several data mining techniques have been developed and used in data mining projects which includes classification, clustering, association rules, prediction, and sequential patterns. Data mining applications are used in various areas such as sales, marketing, banking, finance, health care, insurance and medicine. There are various research domains in data mining namely web mining, text mining, image mining, sequence mining, privacy preserving data mining, etc. Text mining is a technique which extracts information from both structured and unstructured data and also finding patterns which is novel and not known earlier. It is also known as knowledge discovery from text (KDT), deals with the machine supported analysis of text. Text mining is used in various areas such as information retrieval, document similarity, natural language processing and so on. Searching for similar documents is an important problem in text mining. The first and essential step of document similarity is to classify the documents based on their category. In this research work, we have analysed the performance of Bayesian and Lazy classifiers for classifying the files which are stored in the computer hard disk. There are two algorithms in Bayesian classifier namely BayesNet, and Naive Bayes. In lazy classifier has three algorithms namely IBL, IBK and Kstar. The performances of Bayesian and lazy classifiers are analysed by applying various performance factors. From the experimental results, it is observed that the lazy classifier is more efficient than Bayesian classifier.