Agents and data mining: mutual enhancement by integration
06 Jun 2005-Vol. 3505, pp 50-61
TL;DR: This paper tells a story of synergism of two cutting edge technologies — agents and data mining, and a new way to integrate these two techniques –ontology-based integration is discussed.
Abstract: This paper tells a story of synergism of two cutting edge technologies — agents and data mining. By integrating these two technologies, the power for each of them is enhanced. Integrating agents into data mining systems, or constructing data mining systems from agent perspectives, the flexibility of data mining systems can be greatly improved. New data mining techniques can add to the systems dynamically in the form of agents, while the out-of-date ones can also be deleted from systems at run-time. Equipping agents with data mining capabilities, the agents are much smarter and more adaptable. In this way, the performance of these agent systems can be improved. A new way to integrate these two techniques –ontology-based integration is also discussed. Case studies will be given to demonstrate such mutual enhancement.
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Book•
20 Jan 2010TL;DR: Domain Driven Data Mining enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence.
Abstract: In the present thriving global economy a need has evolved for complex data analysis to enhance an organizations production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. About this book: Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence. Examines real-world challenges to and complexities of the current KDD methodologies and techniques. Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications Includes techniques, methodologies and case studies in real-life enterprise data mining Addresses new areas such as blog mining Domain Driven Data Mining is suitable for researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management.
112 citations
TL;DR: This article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference and illustrates some examples in mining actionable correlations in Australian Stock Exchange.
Abstract: Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, very often the knowledge discovered generally is not interesting to real business needs. Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference. The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraintbased context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We also illustrate some examples in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns for practical use by industry and business.
54 citations
Cites background from "Agents and data mining: mutual enha..."
...To this end, agent-based data mining infrastructure (Klusch et al., 2003; Zhang et al., 2005) presents good facilities, since it provides good supports for autonomous problem-solving, user modeling, and user agent interaction....
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03 Jun 2007
TL;DR: This paper draws a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family, and summarizes key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent- mining interaction.
Abstract: In the past twenty years, agents (we mean autonomous agent and multi-agent systems) and data mining (also knowledge discovery) have emerged separately as two of most prominent, dynamic and exciting research areas. In recent years, an increasingly remarkable trend in both areas is the agent-mining interaction and integration. This is driven by not only researcher's interests, but intrinsic challenges and requirements from both sides, as well as benefits and complementarity to both communities through agent-mining interaction. In this paper, we draw a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family. To promote it as a newly emergent scientific field, we summarize key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent-mining interaction. Both theoretical and application-oriented aspects are addressed. The above investigation shows that the agent-mining interaction is attracting everincreasing attention from both agent and data mining communities. Some complicated challenges in either community may be effectively and efficiently tackled through agent-mining interaction. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives.
53 citations
Cites background from "Agents and data mining: mutual enha..."
...After years of unorganized development of the above discussed one-way effect, people further recognize fundamental mutual issues in agent-mining interaction [4,9,33,2], which involve common issues of both parties....
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09 Apr 2006
TL;DR: In this paper, a domain-driven in-depth pattern discovery (DDID-PD) approach is proposed to discover knowledge satisfying real business needs, which can be applied to mining actionable trading strategies in Australian stock exchange data.
Abstract: Actionable knowledgediscovery is one of Grand Challenges in KDD. To this end, many methodologies have been developed. However, they either view data mining as an autonomous data-driven trial-and-error process, or only analyze the issues in an isolated and case-by-case manner. As a result, the knowledge discovered is often not actionable to constrained business. This paper proposes a practical perspective, referred to as domain-driven in-depth pattern discovery (DDID-PD). It presents a domain-driven view of discovering knowledge satisfying real business needs. Its main ideas include constraint mining, in-depth mining, human-cooperated mining, and loop-closed mining. We demonstrate its deployment in mining actionable trading strategies in Australian Stock Exchange data.
48 citations
Journal Article•
TL;DR: This paper proposes a practical perspective, referred to as domain-driven in-depth pattern discovery (DDID-PD), which presents a domain- driven view of discovering knowledge satisfying real business needs and demonstrates its deployment in mining actionable trading strategies in Australian Stock Exchange data.
Abstract: Actionable knowledge discovery is one of Grand Challenges in KDD. To this end, many methodologies have been developed. However, they either view data mining as an autonomous data-driven trial-and-error process, or only analyze the issues in an isolated and case-by-case manner. As a result, the knowledge discovered is often not actionable to constrained business. This paper proposes a practical perspective, referred to as domain-driven in-depth pattern discovery (DDID-PD). It presents a domain-driven view of discovering knowledge satisfying real business needs. Its main ideas include constraint mining, in-depth mining, human-cooperated mining, and loop-closed mining. We demonstrate its deployment in mining actionable trading strategies in Australian Stock Exchange data.
46 citations
References
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Book•
25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Abstract: Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
20,196 citations
Book•
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01 Jan 2008
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.
Abstract: tic regression, and it concerns studying the effect of covariates on the risk of disease. The chapter includes generalized estimating equations (GEE’s) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC. As a prelude to the following chapter on repeated-measures data, Chapter 5 presents time series analysis. The material on repeated-measures analysis uses linear additive models with GEE’s and PROC MIXED in SAS for linear mixed-effects models. Chapter 7 is about survival data analysis. All computing throughout the book is done using SAS procedures.
9,995 citations
01 Mar 2002
TL;DR: This presentation discusses the design and implementation of machine learning algorithms in Java, as well as some of the techniques used to develop and implement these algorithms.
Abstract: 1. What's It All About? 2. Input: Concepts, Instances, Attributes 3. Output: Knowledge Representation 4. Algorithms: The Basic Methods 5. Credibility: Evaluating What's Been Learned 6. Implementations: Real Machine Learning Schemes 7. Moving On: Engineering The Input And Output 8. Nuts And Bolts: Machine Learning Algorithms In Java 9. Looking Forward
5,936 citations
Proceedings Article•
01 Feb 1996
2,710 citations
TL;DR: In this paper, a survey of the available data mining techniques is provided and a comparative study of such techniques is presented, based on a database researcher's point-of-view.
Abstract: Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information-providing services, such as data warehousing and online services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided and to increase business opportunities. In response to such a demand, this article provides a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided and a comparative study of such techniques is presented.
2,327 citations