Leon Shyue-Liang Wang

Mining high-utility itemsets based on particle swarm optimization

High-utility itemset mining (HUIM) is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). Several algorithms have been presented to mine high-utility itemsets (HUIs) and most of the designed algorithms have to handle the exponential search space for discovering HUIs when the number of distinct items and the size of database are very large. In the past, a heuristic HUPEumu-GRAM algorithm was proposed to mine HUIs based on genetic algorithm (GA). For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approach. Since the traditional PSO mechanism is used to handle the continuous problem, in this paper, the discrete PSO is adopted to encode the particles as the binary variables. An efficient PSO-based algorithm namely HUIM-BPSOsig is proposed to efficiently find HUIs. It first sets the number of discovered high-transaction-weighted utilization 1-itemsets (1-HTWUIs) as the size of a particle based on transaction-weighted utility (TWU) model, which can greatly reduce the combinational problem in evolution process. The sigmoid function is adopted in the updating process of the particles of the designed HUIM-BPSOsig algorithm. Substantial experiments on real-life datasets show that the proposed algorithm has better results compared to the state-of-the-art GA-based algorithm.

Social Network Mining, Analysis and Research Trends: Techniques and Applications

Social network analysis dates back to the early 20th century, with initial studies focusing on small group behavior from a sociological perspective. The emergence of the Internet and subsequent increase in the use of online social networking applications has caused a shift in the approach to this field. Faced with complex, large datasets, researchers need new methods and tools for collecting, processing, and mining social network data.Social Network Mining, Analysis and Research Trends: Techniques and Applications covers current research trends in the area of social networks analysis and mining. Containing research from experts in the social network analysis and mining communities, as well as practitioners from social science, business, and computer science, this book proposes new measures, methods, and techniques in social networks analysis and also presents applications and case studies in this changing field.

A Swarm-Based Approach to Mine High-Utility Itemsets

High-utility itemset mining (HUIM) is a critical issue in recent years since it can reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM) or association-rule mining (ARM). In the past, a GA-based appraoch was designed to mine HUIs. It suffers, however, the combinational problem to assign the initial chromosomes for later evolution process. Besides, it is a non-trivial task to find the appropriate parameters for GA-based mechanism. In this paper, a binary PSO-based algorithm is thus proposed to efficiently find HUIs. A sigmoid function is adopted in the designed algorithm in the evolution process for discovering HUIs. Substantial experiments on real-life datasets show that the proposed algorithm has better results compared to the state-of-the-art GA-based algorithm of HUIM in terms of execution time and number of discovered HUIs.

The 8th International Conference on Knowledge Management in Organizations

The concept of servitization ‘adding value by adding services to products’ was first introduced by Vandermerwe and Rada in 1988, which in later became a popular topic for researchers in the academia, business and government. Today, it is widely recognized as an increasingly relevant business strategy for manufacturing firms to improve their competitive advantage in the market. In many cases, the necessity or application of servitization concept explained by researchers from organization perspective, especially for developed economy, but they were less attentive to discuss the issue from customer viewpoint in developing economy. Therefore, this paper aims to examine the needs of servitization from customer perspectives, particularly the IT industry of emerging market ‘Bangladesh’. The data was collected by the interviews of suppliers and customers in the IT industry of Bangladesh. The survey results showed that the current suppliers cannot satisfy the customer needs at this moment, because customers are not happy anymore with the IT goods only; they also require solutions, knowledge and reliability as well. Z. Ahamed (&) T. Inohara Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, 152-8552 Tokyo, Japan e-mail: Zahir@valdes.titech.ac.jp T. Inohara e-mail: inohara@valdes.titech.ac.jp A. Kamoshida Kyoto University, Yoshida Hon Machi, Sakyo-ku, 606-8501 Kyoto, Japan e-mail: kamoshida@gsm.kyoto-u.ac.jp L. Uden et al. (eds.), The 8th International Conference on Knowledge Management in Organizations, Springer Proceedings in Complexity, DOI: 10.1007/978-94-007-7287-8_1, Springer Science+Business Media Dordrecht 2014 3

The 3rd International Workshop on Intelligent Data Analysis and Management

These papers on Intelligent Data Analysis and Management (IDAM) examine issues related to the research and applications of Artificial Intelligence techniques in data analysis and management across a variety of disciplines. The papers derive from the 2013 IDAM conference in Kaohsiung ,Taiwan. It is an interdisciplinary research field involving academic researchers in information technologies, computer science, public policy, bioinformatics, medical informatics, and social and behavior studies, etc. The techniques studied include (but are not limited to): data visualization, data pre-processing, data engineering, database mining techniques, tools and applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing, etc.

Model of Integration of Business Intelligence and Knowledge Management

The correct integration between business intelligence (BI) and knowledge management (KM) allows organizations to make decisions in accordance with their strategic objectives, through accurate and timely information. The present article presents a model that uses emerging technologies to integrate both tools, considering also the technological and knowledge assets (which are key elements in decision making) in order to enable the cognitive download of company employees, innovation and operational excellence.

Special issue on intelligent analytics and management of mobile data and social media

In the past decade, we have witnessed the explosive growth of mobile devices, including notebooks, smartphones, sensors, tablets and wearable devices, and social media that allows the creation and exchange of user-generated content, such as YouTube, Facebook, Twitter, Flickr, to name a few. Now we are experiencing the blending of these two technologies, forming a widely adopted platform that reshapes nearly all aspects of our daily life, e.g., the way we connect people, doing business, entertaining, etc. This inspires enterprises to seek new business models and provides them greater opportunities than ever before to understand their customers, competitors, marketing trend by analyzing and managing data collected or created during the usage of mobile services and social media. However, specific characteristics of these data and media, like high variance in quality and usability, large volume, high velocity, and low permanence make most contemporary data analysis and managing tools awkward to meet the enterprises’ expectations.

Toward Construction of Open Election Data Model: A Case Study of Taiwan's Election Data

Open election data is an emerged domain in open data for it reflects the maturity of governance and democracy. On this regard, a standardized approach for building open election data will significantly reduce the time and effort for publishing election data to the public for easy access. In this work, we will develop an initial step for building open election data, namely building election data model. The procedure and preliminary result of such task on Taiwans election data will be described. We believe the process on building election data model could be beneficial to other authorities which intend to publicize their election data.

Multidisciplinary Social Networks Research

The rise of Social Networking Services (SNSs) has not only transformed people as well as consumer behavior on the Internet, but also transformed the means by which various enterprises globally conduct their promotional and marketing campaigns. There are a variety of means by which enterprises have launched their marketing campaigns on Social Networking Services, and one of the most common techniques adopted is through extensive advertising campaigns on SNSs. This study seeks to examine consumer behaviors towards advertisements on Social Networking Services. Key factors affecting consumer behaviors include usage pattern, the credibility of a particular Social Networking Service as well as electronic word-of-mouth. This clearly illustrates that in today’s virtual electronic world, social media have progressed from being merely a place to meet people, to being a virtual sales floor. It is unexpected that consumer behaviors are influenced by the electronic word-of-mouth of friends rather than that of strangers.

Recent Studies in Privacy Preservation

In recent years, social network research has advanced significantly. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of social network analysis and mining in academia, politics, homeland security and business. However, growing popularity of on-line social networking and data publishing, not only brings the convenience of information sharing but also concerns of privacy breaches, as sensitive information, through links and inferences, individuals identity, health, financial status, political affiliations, etc, might be disclosed. In order to preserve privacy of users, anonymization is required prior to attempts to make the data more widely available to public. In this talk, we will concentrate on introducing recent studies in privacy preservation in data publishing, data mining, networking publishing, location publishing, and mobile publishing.