期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2011
卷号：33
期号：2
页码：193-196
出版社：Journal of Theoretical and Applied
摘要：There are several efficient methods for the discovery or mining of various types of data, methods devised for mining textual static web content have always been proved less efficient due to the data's ambiguous , unclassified, unstructured or un-clustered nature. Various association rule mining algorithms like Generalized pattern algorithm are being implemented to mine the web content but again due to the above setbacks the efficiency expected from the algorithm is not obtained. Since the dip in the efficiency of these algorithms is amounted to the nature of the textual web content, an algorithm which may deal with , if not all the anomalies at least the un-clustered nature of the content may increase the efficiency drastically. This paper emphasizes the same point, making the assumptions that the web content is static and there is at least one common pattern found in the given datasets.
