I-Hsien Ting

Web mining techniques for on-line social networks analysis

On-line social networking has become a very popular Web 2.0 application. This paper studies the issues around using web mining techniques for analysis of on-line social networks. Techniques and concepts of web mining and social networks analysis will be introduced and reviewed along with a discussion about how to use web mining techniques for on-line social networks analysis. In addition this paper sets out a process to use web mining for on-line social networks analysis, which can be treated as a general process in this research area. Discussions of the challenges and future research are also included.

Web Mining Applications in E-commerce and E-services

Web mining applications in E-commerce and E-services is a new research direction in the area of web mining. Among all of the possible applications in web research, e-commerce and e-services have been identified as important domains for Web-mining techniques. Web-mining techniques also play an important role in e-commerce and eservices, proving to be useful tools for understanding how ecommerce and e-service Web sites and services are used. This book therefore collects new developments and high quality researches for the readers of this book to understand the topics of web mining applications in e-commerce and e-services as well as the state-of-the-arts in this area. The chapters in this book include web usage mining and user browsing behavior analysis, semantic web mining, web performance mining, web mining for users need understanding, web mining for social network analysis and web mining for P2P services.

Analyzing Multi-source Social Data for Extracting and Mining Social Networks

In recent years, social computing has become a very popular application in the Internet, and therefore large amount of social (communication) data has been collected in differentsocial computing application. This paper will introduce a methodology to collect and analyze multi-source social, and by this for extracting social networks from the data. A system architecture will also be presented in this paper to show how the data can be collected, pre-processed, analyzed. Furthermore, the system will allow the users to use the data as a resource for personal decision support.

Anonymizing shortest paths on social network graphs

Social networking is gaining enormous popularity in the past few years. However, the popularity may also bring unexpected consequences for users regarding safety and privacy concerns. To prevent privacy being breached and modeling a social network as a weighted graph, many effective anonymization techniques have been proposed. In this work, we consider the edge weight anonymity problem. In particular, to protect the weight privacy of the shortest path between two vertices on a weighted graph, we present a new concept called k-anonymous path privacy. A published social network graph with k-anonymous path privacy has at least k indistinguishable shortest paths between the source and destination vertices. Greedy-based modification algorithms and experimental results showing the feasibility and characteristics of the proposed approach are presented.

Constructing a Cloud Computing Based Social Networks Data Warehousing and Analyzing System

The research area of Social networks analysis has been recognized as extremely time-consuming tasks as well as large storage space is always necessary in order to store the social data, especially to deal with the data in the World Wide Web. Therefore, how to design an architecture and environment for performing social networks analysis is very essential. In this paper, we proposed a data warehousing and analyzing system which is based on the concept of cloud computing. The system has also been implemented and evaluated under the proposed environment with different cloud computing approaches.

Performance analysis of classification algorithms on early detection of liver disease

In this research UCI Indian Liver Patient Dataset (ILPD) used.Boosted C5.0 and CHAID algorithms are used to identify liver disease risk factors.This research shows females have more chance of liver disease than males.Common risk factors of liver disease were extracted by data mining.This research produced quite simple rules. The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area.In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UCI instances of liver disease dataset from the UCI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted C5.0 and CHAID algorithms. Until now there is no work in the literature that uses boosted C5.0 and CHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB=10.900-1.200, ALB 4.00-4.300, SGPT=34-37, TB=0.600-1.200 (by boosted C5.0), A/G=1.180-1.390, as well as in the Boosted C5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that C5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the CHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted C5.0 and CHAID algorithms are capable to produce rules for liver disease. Our results also show that boosted C5.0 considers the gender in liver disease, a factor which is missing in many other studies. Meanwhile, using the rules generated in boosted C5.0 algorithm, we obtained the important result about low susceptibility of female to liver disease than male. This factor is missing in other studies of liver disease. Therefore, our proposed computer-aided diagnostic methods as an expert and intelligent system have impressive impact on liver disease detection. Based on obtained results, we observed that our model had better performance compared to existing methods in the literature.

Identifying web navigation behaviour and patterns automatically from clickstream data

A users clickstream, such as that which is found in server-side logs, can be a rich source of data concerning the ways in which a user navigates a site, but the volume and level of detail found in these logs makes it difficult to identify and categorise specific navigational patterns. In this paper, we describe the three-step automatic pattern discovery (APD) method, a tool that utilises sequential mining to extract a users navigation route based on two levels of basic navigational elements. This paper contains descriptions of two studies in which the APD was used; the first makes use of APD to analyse the usage of an educational website; the second describes how APD was used to improve the design of a technical support website in a university department.A users clickstream, such as that which is found in server-side logs, can be a rich source of data concerning the ways in which a user navigates a site, but the volume and level of detail found in...

Web Mining Techniques for On-Line Social Networks Analysis: An Overview

On-line social networking has become a very popular application of Web 2.0 ages. This chapter provides a study about the issues of using web mining techniques for on-line social networks analysis. Techniques and concepts of web mining and social networks analysis will be introduced and reviewed in this chapter as well as a discussion about how to use web mining techniques for on-line social networks analysis. Moreover, in this chapter, a process to use web mining for on-line social networks analysis is proposed, which can be treated as a general process in this research area. Discussions of the challenges and future research are also included in this chapter.

Applying Web Usage Mining Techniques to Discover Potential Browsing Problems of Users

In this paper, a web usage mining based approach is proposed to discover potential browsing problems. Two web usage mining techniques in the approach are introduced, including Automatic Pattern Discovery (APD) and Co-occurrence Pattern Mining with Distance Measurement (CPMDM). A combination method is also discussed to show how potential browsing problems can be identified

Content matters: a study of hate groups detection based on social networks analysis and web mining

In recent years, with rapid growth of social networking websites, users are very active in these platforms and large amount of data are aggregated. Among those social networking websites, Facebook is the most popular website that has most users. However, in Facebook, the abusing problem is a very critical issue, such as Hate Groups. Therefore, many researchers are devoting on how to detect potential hate groups, such as using the techniques of social networks analysis. However, we believe content is also a very important factors for hate groups detection. Thus, in this paper, we will propose an architecture to for hate groups detection which is based on the technique of Social Networks Analysis and Web Mining (Text Mining; Natural Language Processing). From the experiment result, it shows that content plays an critical role for hate groups detection and the performance is better than the system that just applying social networks analysis.