Абстрактный

Comparative Study on Classification Meta Algorithms

Dr. S. Vijayarani Mrs. M. Muthulakshmi

Data mining is one of the most important research areas in the field of computer science. Data mining techniques are used for extracting the hidden knowledge from the large databases. There are various research domains in data mining such as image mining, text mining, sequential pattern mining, web mining, and so on. The purpose of text mining is to process unstructured information, extract meaningful numeric indices from the text and thus make the information contained in the text accessible to the various data mining algorithms. There are various methods in text mining such as information retrieval, document similarity, information extraction, clustering, classification, and so on. Searching of similar documents has an important role in text mining and document management. Classification is one of the main tasks in document similarity. It is used to classify the documents based on their category. In this research work, we have analyzed the performance of three Meta classification algorithms namely Attribute Selected Classifier, Filtered Classifier and LogitBoost. These algorithms are used for classifying computer files based on their extension. For example – pdf, txt, doc, ppt, xls and so on. The performances of Meta algorithms are analyzed by applying performance factors such as classification accuracy and error rate. From the experimental results, it is analyzed that LogitBoost performs better than other algorithms.