Extracting Web User Profiles Using a Modified CARD Algorithm

Clustering algorithms are widely used methods for organizing data into useful information. The Competitive Agglomeration for Relational Data (CARD) Algorithm is one such clustering algorithm that is designed to organize user sessions into profiles, where each profile would highlight a particular type of user. The CARD algorithm is a viable candidate for web clustering; however, it does have limitations such as an extended execution time. In addition, the methods that prepare the input data for the CARD algorithm’s use employs concepts which seem to be incomplete. These limitations of the CARD algorithm are explored and modifications are introduced to yield a more practical and efficient algorithm

A Modified Competitive Agglomeration for Relational Data Algorithm

Clustering algorithms are invaluable methods for organizing data into useful information. The CARD Algorithm [11 is one such algorithm that is designed to organize user sessions into profiles, where each profile would highlight a particular type of user. The CARD algorithm is a viable candidate for web clustering. However it does have limitations such as long execution time. In addition, the data preparation for the algorithm\u27s requirements employs concepts that are incomplete. These limitations of the algorithm will be explored and modified to yield a more practical and efficient algorithm

A Survey on Web Usage Mining

Now a day World Wide Web become very popular and interactive for transferring of information. The web is huge, diverse and active and thus increases the scalability, multimedia data and temporal matters. The growth of the web has outcome in a huge amount of information that is now freely offered for user access. The several kinds of data have to be handled and organized in a manner that they can be accessed by several users effectively and efficiently. So the usage of data mining methods and knowledge discovery on the web is now on the spotlight of a boosting number of researchers. Web usage mining is a kind of data mining method that can be useful in recommending the web usage patterns with the help of users2019; session and behavior. Web usage mining includes three process, namely, preprocessing, pattern discovery and pattern analysis. There are different techniques already exists for web usage mining. Those existing techniques have their own advantages and disadvantages. This paper presents a survey on some of the existing web usage mining techniques

A Comparative Study of Different Log Analyzer Tools to Analyze User Behaviors

With the explosive growth of information available on internet, WWW become the most powerful platform to broadcast, store and retrieve information. As many people move to internet to gather information, analyzing user behavior from web access logs can be helpful to create adaptive system, recommender system and intelligent e-commerce applications. Web access log files are the files that contain information about interaction between users and the websites with the use of internet. It contains the details like User name, IP Address, Time Stamp, Access Request, number of bytes transferred, result status, URL that referred. To analyze such user behavior, a variety of analyzer tools exist. This paper provides a comparative study between famous log analyzer tools based on their features and performance. DOI: 10.17762/ijritcc2321-8169.150510

Data mining in soft computing framework: a survey

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

The need to use data mining techniques in E-Business

Abstract. The number of Internet users rose from 400 million in 2000 to just over 2 billion in early 2011. This means that approximately one third of the world's population uses the internet. Taking  these conditions into consideration, we can say that businesses have changed their way. Many companies that, over the last century could not even dream that could have a certain volume of activity or they could face competition with industry giants, have succeeded in giving to enjoy great success.  For example: Amazon.com, founded in 1995, had in 1999 a turnover of at least 13 times higher than other prestigious names in the U.S., such as Barnes & Noble and Borders Books & Music. E-business is the key to make life easier for the people. Knowledge of e-business environment is essential for doing business in this century. More must be understood and new technologies applied to extract knowledge from data

An Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents

The Proposed System Analyses the scopes introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the 201C;noise.201D; Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. We propose an advanced data mining method using SVD that combines both tag and value similarity, item and user preference. SVD automatically extracts data from query result pages by first identifying and segmenting the query result records in the query result pages and then aligning the segmented query result records into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the query result records based on user preferences, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested-structure that may exist in the query result records

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment