Islamic Azad University

2014

There are many models in literature and practice that analyse user behaviour based on user navigation data and use clustering algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user’s interests. In this paper, we model user behaviour as a vector of the time the user spends at each URL, and further classify a given new user access pattern. The clustering and classification methods of k-means with non-Euclidean similarity measure, Bayesian classifiers and artificial neural networks, with standardised fuzzy inputs are implemented and compared. Apart from identifying user behaviour, the model can also be used as a prediction system where we can identify deviational behaviour. Keywords:

American Journal of Applied Sciences, 2008

Worldwide web applications have grown very rapidly and have made a significant impact on computer systems. Among them, web browsing for useful information may be most commonly seen. Due to its tremendous amounts of use, efficient and effective web retrieval has become a very important research topic in this field. Techniques of web mining have thus been requested and developed to achieve this purpose. In this research, a new fuzzy weighted web-mining algorithm is proposed, which can process web-server logs to discover useful users' browsing behaviors from the time durations of the paged browsed. Since the time durations are numeric, fuzzy concepts are used here to process them and to form linguistic terms. Besides, different web pages may have different importance. The importance of web pages are evaluated by managers as linguistic terms, which are then transformed and averaged as fuzzy sets of weights. Each linguistic term is then weighted by the importance for its page. Only the linguistic term with the maximum cardinality for a page is chosen in later mining processes, thus reducing the time complexity. The minimum support is set linguistic, which is more natural and understandable for human beings. An example is given to clearly illustrate the proposed approach.

2011 International Conference on Computer and Management (CAMAN), 2011

In this paper we present clustering method is very sensitive to the initial center values ,requirements on the data set too high, and cannot handle noisy data the proposal method is using information entropy to initialize the cluster centers and introduce weighting parameters to adjust the location of cluster centers and noise problems. The navigation datasets which are sequential in nature, Clustering web data is finding the groups which share common interests and behavior by analyzing the data collected in the web servers, this improves clustering on web data efficiently using improved fuzzy c-means(FCM) clustering. Web usage mining is the application of data mining techniques to web log data repositories. It is used in finding the user access patterns from web access log. Web data Clusters are formed using on MSNBC web navigation dataset.

Management Science Letters, 2015

Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user's behavior that is considered as a way to discover the potential knowledge of web user's interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users' requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user's interest and to propose a list of pages based on their interests by identifying user's behavior based on fuzzy techniques called fuzzy clustering method. Due to the user's different interests and use of one or more interest at a time, user's interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user's movement pattern and using neural network a list of suggested pages to the users is provided.

2003

Direrent users have diflerent needs from the same web page and hence 2 is necessary to develop asystem which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these w b server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen's self organizing maps @OM) and interpreting those results using Unified distance Matrix (U-matrix).These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with similar browsing patterns. These clusters are useful in web personalization so that it communicates better with its users and also in web traffic analysis for predicting web traffic at a given period of time.

The World Wide Web continues to grow at an amazing rate in both the size and complexity of Web sites and is well on it's way to being the main reservoir of information and data. Due to this increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. To design popular and attractive websites publishers must understand their users' needs. Therefore analyzing users' behaviour is an important part of web page design. Web Usage Mining (WUM) is the application of datamining techniques to web usage log repositories in order to discover the usage patterns that can be used to analyze the user's navigational behavior [1]. WUM contains three main steps: preprocessing, knowledge extraction and results analysis. The goal of the preprocessing stage in Web usage mining is to transform the raw web log data into a set of user profiles. Each such profile captures a sequence or a set of URLs representing a user session.

International Journal of Intelligent Engineering and Systems, 2018

In day to day life, the process of projecting the correct information to the authorized person is more difficult, which makes complexity to the decision making process. Web Page Recommendation Systems (WPRS) used in various fields to identify the customer needs and to help the users to take appropriate decisions over the service or product according to his/her preference. The group of users with similar preference will be identified by using Possibilistic Fuzzy C-Means (PFCM) algorithm with an S3I Similarity Measure (SM). The proposed method will determine the gain and loss of the web users based on the web directories which can be modified by using Relevance Feedback Bayesian Network (RFBN) technique. The experimental results are conducted on the MNSBC dataset and the outcomes are compared with the existing methods like Singular Value Decomposition (SVD) methods. The method predicts the accuracy up to 85% when compared with the existing methods and the outcome results proved the effectiveness of the PFCM-RFBN method.

The log data at a search engine can be used to analyze users’ search behavior and to develop search technologies to improve users’ search experiences. Web usage mining performs mining on web usage data or web logs. A web log is a listing of page reference data/clickstream data. The behavior of the web page readers is imprint in the web server log files. By using the sequence of pages a user accesses, a user profile could be developed thus used in personalization. With personalization, web access or the contents of web page are modified to better fit the desires of the user and also to identify the browsing behavior of the user can improve system performance, enhance the quality and delivery of Internet Information services to the end user, and identify the population of potential customers. For this purpose a new clustering based approach is used, The proposed algorithm finds the meaningful behavior patterns extracted by applying efficient clustering algorithm, to log data. It is proved that performance of the proposed system is better than that of the existing algorithm. The proposed algorithm can provide popular information from web page visitors for web personalization.

Due to the continuous increase in growth and complexity of WWW, web site publishers are facing increasing difficulty in attracting and retaining users. In order to design attractive web sites, designers must understand their users' needs. Therefore analysing navigational behaviour of users is an important part of web page design. Web Usage Mining (WUM) is the application of data mining techniques to web usage data in order to discover the patterns that can be used to analyse the user's navigational behaviour. Preprocessing, knowledge extraction and results analysis are the three main steps of WUM. Due to large amount of irrelevant information present in the web logs, the original log file can not be directly used in the WUM process. During the preprocessing stage of WUM raw web log data is to transformed into a set of user profiles. Each user profile captures a set of URLs representing a user session. This sessionized data can be used as the input for a variety of data mining tasks such as clustering, association rule mining, sequence mining etc. If the data mining task at hand is clustering, the session files are filtered to remove very small sessions in order to eliminate the noise from the data. But direct removal of these small sized sessions may result in loss of a significant amount of information specially when the number of small sessions is large. We propose a "Fuzzy Set Theoretic" approach to deal with this problem. Instead of directly removing all the small sessions below a specified threshold, we assign weights to all the sessions using a "Fuzzy Membership Function" based on the number of URLs accessed by the sessions. After assigning the weights we apply a "Fuzzy c-Mean Clustering" algorithm to discover the clusters of user profiles. In this paper, we provide a detailed review of various techniques to preprocess the web log data including data fusion, data cleaning, user identification and session identification. We also describe our methodology to perform feature selection (or dimensionality reduction) and session weight assignment tasks. Finally we compare our soft computing based approach of session weight assignment with the traditional hard computing based approach of small session elimination.

2016

The main problem faced by the users of web search today is the quality and the amount of the results they get back. The results frustrate a user and consume his precious time. The objective of a web personalization system is to provide users with the information they want or need, without expecting from them to ask for it explicitly. A web recommender system is a web-based interactive software agent. A WRS attempts to predict user preferences from user data and/or user access data for the purpose of facilitating and personalizing users’ experience on-line by providing them with recommendation lists of suggested items. This research proposes a new personalized recommendation system integrating clustering and association rule technique. This system improves the recommendation quality of system and save time of recommendation process. It also overcomes the drawbacks of traditional recommendation system.