Mining Matrix Pattern from Mobile Users

Mining Matrix Pattern from Mobile Users

John Goh (Monash University, Australia)
DOI: 10.4018/978-1-60566-144-5.ch013
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Mobile user data mining is about extracting knowledge from raw data collected from mobile users. There have been a few approaches developed, such as frequency pattern (Goh & Taniar, 2004), group pattern (Lim, Wang, Ong, et al., 2003; Wang, Lim, & Hwang, 2003), parallel pattern (Goh & Taniar, 2005) and location dependent mobile user data mining (Goh & Taniar, 2004). Previously proposed methods share the common drawbacks of costly resources that have to be spent in identifying the location of the mobile node and constant updating of the location information. The proposed method aims to address this issue by using the location dependent approach for mobile user data mining. Matrix pattern looks at the mobile nodes from the point of view of a particular fixed location rather than constantly following the mobile node itself. This can be done by using sparse matrix to map the physical location and use the matrix itself for the rest of mining process, rather than identifying the real coordinates of the mobile users. This allows performance efficiency with slight sacrifice in accuracy. As the mobile nodes visit along the mapped physical area, the matrix will be marked and used to perform mobile user data mining. The proposed method further extends itself from a single layer matrix to a multi-layer matrix in order to accommodate mining in different contexts, such as mining the relationship between the theme of food and fashion within a geographical area, thus making it more robust and flexible. The performance and evaluation shows that the proposed method can be used for mobile user data mining.
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Data mining (Agrawal & Srikant, 1994, 1995; Chen & Liu, 2005; Xiao, Yao, & Yang, 2005) is the field of research which aims to extract useful and interesting patterns out from source datasets supplied to the algorithm. Data mining is an emerging field which allows organisations such as business and government who have a huge amount of datasets stored in very large database to be able to benefit from the algorithms by converting datasets into patterns and eventually studied and becomes useful knowledge. Data mining is still an ongoing research, and previously available outcomes from data mining include association rules, sequential patterns which derives useful patterns by analysing market basket (Agrawal & Srikant, 1994, 1995), which is the list of items customers buy in a supermarket. Other previously proposed methods in data mining includes time series analysis (Barbar’a, Chen, & Nazeri, 2004; Han, Dong, & Yin, 1999; Han, Gong, & Yin 1998), brain analysis (Claude, Daire, & Sebag, 2004), Web log pattern analysis (Christophides, Karvounarakis, & Plexousakis, 2003; Eirinaki & Vazirgaiannis, 2003; Wilson & Matthews, 2004), increasing overall efficiency of data mining in very large databases (Han, Pei, & Yin, 2000; Li, Tang, & Cercone, 2004; Thiruvady & Webb, 2004), data mining on data warehouses (Tjioe & Taniar, 2005), security of private data in data mining (Oliveira, Zaiane, & Saygin, 2004) and spatial, location dependent data mining (Hakkila & Mantyjarvi, 2005; Koperski & Han, 1995; Lee, Xu, Zheng, & Lee, 2002; Tse, Lam, Ng, & Chan, 2005).

Mobile user data mining (Goh & Tanair, 2004a, 2004b, 2005; Lee, Xu, Zheng, & Lee, 2002; Lim, Wang, Ong, et al., 2003) is an extension of data mining which specializes in looking at how useful patterns can be derived from the raw datasets collected from mobile users. In a mobile environment, two types of entities can usually be found: static nodes, which are fixed entities such as the wireless access points, and mobile nodes, which are the mobile entities which have the flexibility to move along in the environment, such as the personal digital assistant, mobile phones, and laptop computers. The raw datasets from mobile users comes from the physical movement logs of mobile users, the items that mobile users purchased over time, the location of static nodes and their properties and the context in which the mobile users went into over a timeframe.

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Table of Contents
Vijayan Sugumaran
Chapter 1
Hong Lin
In this chapter a program construction method based on ?-Calculus is proposed. The problem to be solved is specified by first-order predicate logic... Sample PDF
Designing Multi-Agent Systems from Logic Specifications: A Case Study
Chapter 2
Rahul Singh
Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers.... Sample PDF
Multi-Agent Architecture for Knowledge-Driven Decision Support
Chapter 3
Farid Meziane
Trust is widely recognized as an essential factor for the continual development of business-to-customer (B2C) electronic commerce (EC). Many trust... Sample PDF
A Decision Support System for Trust Formalization
Chapter 4
Mehdi Yousfi-Monod
The work described in this chapter tackles learning and communication between cognitive artificial agents and trying to meet the following issue: Is... Sample PDF
Using Misunderstanding and Discussion in Dialog as a Knowledge Acquisition or Enhancement Procecss
Chapter 5
Sungchul Hong
In this chapter, we present a two-tier supply chain composed of multiple buyers and multiple suppliers. We have studied the mechanism to match... Sample PDF
Improving E-Trade Auction Volume by Consortium
Chapter 6
Manoj A. Thomas, Victoria Y. Yoon, Richard Redmond
Different FIPA-compliant agent development platforms are available for developing multiagent systems. FIPA compliance ensures interoperability among... Sample PDF
Extending Loosely Coupled Federated Information Systems Using Agent Technology
Chapter 7
H. Hamidi
The reliable execution of mobile agents is a very important design issue in building mobile agent systems and many fault-tolerant schemes have been... Sample PDF
Modeling Fault Tolerant and Secure Mobile Agent Execution in Distributed Systems
Chapter 8
Xiannong Meng, Song Xing
This chapter reports the results of a project attempting to assess the performance of a few major search engines from various perspectives. The... Sample PDF
Search Engine Performance Comparisons
Chapter 9
Antonio Picariello
Information retrieval can take great advantages and improvements considering users’ feedbacks. Therefore, the user dimension is a relevant component... Sample PDF
A User-Centered Approach for Information Retrieval
Chapter 10
Aboul Ella Hassanien, Jafar M. Ali
This chapter presents an efficient algorithm to classify and retrieve images from large databases in the context of rough set theory. Color and... Sample PDF
Classification and Retrieval of Images from Databases Using Rough Set Theory
Chapter 11
Lars Werner
Text documents stored in information systems usually consist of more information than the pure concatenation of words, i.e., they also contain... Sample PDF
Supporting Text Retrieval by Typographical Term Weighting
Chapter 12
Ben Choi
Web mining aims for searching, organizing, and extracting information on the Web and search engines focus on searching. The next stage of Web mining... Sample PDF
Web Mining by Automatically Organizing Web Pages into Categories
Chapter 13
John Goh
Mobile user data mining is about extracting knowledge from raw data collected from mobile users. There have been a few approaches developed, such as... Sample PDF
Mining Matrix Pattern from Mobile Users
Chapter 14
Salvatore T. March, Gove N. Allen
Active information systems participate in the operation and management of business organizations. They create conceptual objects that represent... Sample PDF
Conceptual Modeling of Events for Active Information Systems
Chapter 15
John M. Artz
Earlier work in the philosophical foundations of information modeling identified four key concepts in which philosophical groundwork must be further... Sample PDF
Information Modeling and the Problem of Universals
Chapter 16
Christian Hillbrand
The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect... Sample PDF
Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence
Chapter 17
Yongjian Fu
In this chapter, we propose to use N-gram models for improving Web navigation for mobile users. Ngram models are built from Web server logs to learn... Sample PDF
Improving Mobile Web Navigation Using N-Grams Prediction Models
Chapter 18
Réal Carbonneau, Rustam Vahidov, Kevin Laframboise
Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses.... Sample PDF
Forecasting Supply Chain Demand Using Machine Learning Algorithms
Chapter 19
Teemu Tynjala
The present study implements a generic methodology for describing and analyzing demand supply networks (i.e. networks from a company’s suppliers... Sample PDF
Supporting Demand Supply Network Optimization with Petri Nets
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