A Tool for Classification of Sequential Data (original) (raw)

Classification Technique for Improving User Access on Web Log Data

In the present era, Internet is playing a significant role in our everyday life; therefore, it is very thorny to survive without it. Web log file that keeps track of the users’ access on net, if mined, can provide us precious information about the surfers. Similarly, the rapid growth of data mining applications has shown the necessity for machine learning algorithms to be applied to large-scale data. In this paper, we are using the naïve Bayesian (NB) classification technique using Weka for identifying the frequent access pattern. The main objective of this paper is to categorize browsing behavior of the user based on their position. This paper performs experiment and classifies the user access behavior from the large databases, which could result in increasing the efficiency and effectiveness of the system by reducing the browsing time of the user or results in fast retrieval of information from the system.

Proposing a Classification Algorithm for User Identification According To User Web Log Analysis

Australian Journal of Basic and Applied Sciences

Request classification is one of important strategies of Web. User behavior analysis can make Web service more intelligent and secure. Recently, tree structures have become a popular way for storing and manipulating huge amount of data. The classification of these data can facilitate storage, retrieval, indexing, query answering and different processing operations. In this paper, we propose User-Classifier algorithm for rule based classification of tree structured data that to classify web log user. This algorithm is based on extracting special tree pattern from training dataset. Our experiments show that User-Classifier reduces running time. In the case of complete classification, User-Classifier shows the best classification quality.