Domain driven data mining

by Longbing Cao

Publisher: Springer in New York, London

Written in English
Published: Pages: 248 Downloads: 295
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Subjects:

  • Business,
  • Data processing,
  • Data mining

Edition Notes

Includes bibliographical references (p. 233-243) and index.

StatementLongbing Cao ... [et al.].
Classifications
LC ClassificationsQA76.9.D343 D66 2010
The Physical Object
Paginationxvi, 248 p. :
Number of Pages248
ID Numbers
Open LibraryOL25313182M
ISBN 101441957367, 1441957375
ISBN 109781441957368, 9781441957375
LC Control Number2009942454
OCLC/WorldCa471801285

And also tell him/her that you have a notion of some of the important concepts from Domain-Driven Design. Discuss some. If you don’t want to start by reading a book of pages, you can start here. Afterwards, you will understand what Domain-Driven Design is all about.   His research interest focuses on domain driven data mining, multi-agents, and the integration of agent and data mining. He is a chief investigator of 2 ARC (Australian Research Council) Discovery projects and 1 ARC Linkage project. He has 50+ publications, including 1 monograph, 2 edited books and 10 journal articles. 2. Challenges and directions for a community infrastructure for Big Data-driven research in software architecture. 3. Model clone detection and its role in emergent model pattern mining. 4. Domain-driven analysis of architecture reconstruction methods. Part 2. Methods and tools. 5. Monitoring model analytics over large repositories with Hawk.   Cao L.B Zhang C.Q., Domain-Driven Actionable Knowledge Discovery in the Real World, PAKDD , LNAI , 7. Zhu Domain-Driven Data Mining, International Symposium on Intelligent Information Technology Application Workshops, , IITAW ' Dec. , Shanghai, 44 - 48, 8.

Methods for managing complex software construction following the practices, principles and patterns of Domain-Driven Design with code examples in C# This book presents the philosophy of Domain-Driven Design (DDD) in a down-to-earth and practical manner for experienced developers building applications for complex domains. A focus is placed on the principles and practices of decomposing a.   Domain-driven data mining (D3M) has been proposed to tackle the above issues, and promote the paradigm shift from “data-centered knowledge discovery” to “domain-driven, actionable knowledge delivery.” The rest of the paper is organized as follows. The shift from data driven data mining to domain driven data mining is. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Real-world data mining generally must consider and involve domain and business oriented factors such as human knowledge, constraints and business expectations. This encourages the development of a domain driven methodology to strengthen data-centered pattern mining.

Chapter 1 Introduction Exercises 1. What is data mining?In your answer, address the following: (a) Is it another hype? (b) Is it a simple transformation or application of technology developed from databases, statistics, machine learning, and pattern recognition? (c) We have presented a view that data mining is the result of the evolution of database technology. Domain Driven Data Mining aims to tackle such challenges. The " ACM SIGKDD International Workshop on Domain Driven Data Mining (DDDM)" has provided a premier forum for sharing findings, knowledge, insight, experience and lessons in tackling potential challenges.   Data mining competitions such as Kaggle and KDD have demonstrated the opposite and shown how data science can be successfully outsourced to people without domain expertise. Many companies have run competitions on such diverse topics as optimizing flight routes, predicting ocean health and diabetic retinopathy detection.

Domain driven data mining by Longbing Cao Download PDF EPUB FB2

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:Cited by: 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. Domain Driven Data Mining: /ch Quantitative intelligence based traditional data mining is facing grand challenges from real Cited by: Highlights We review data mining in human resource management as a new and prospering field of research using a domain driven framework.

Findings show a large number of contributions of recent date and mostly technical or methodical provenance (n = ). Contributions cover a broad variety of HR domain problems and data mining methods. Contributions, however, regularly do not consider.

real world, we propose the methodology of Domain Driven Data Mining (D3M for short). D3M aims to construct next-generation methodologies, techniques and tools for a possible paradigm shift from data-centered hidden pattern mining to domain-driven actionable knowledge delivery.

In this talk, we address the concept map of D3M, theoretical. Keywords: Data Mining, Domain driven data mining, decision-making.

INTRODUCTION n the last ten years, data mining is a field which becomes the most active, dynamic and lively area in information and communication technologies. The rapid growth of the global economy and heavy usage of computing and networking across every sector and.

and dependable. Domain driven data mining targets such challenges and objectives. A high level of domain driven data mining framework. has been discussed in [2, 3]. Domain driven, actionable knowledge discovery should involve, support and integrate the following intelligence and constraints: domain.

Domain-driven Data Domain driven data mining book - Free download as Powerpoint Presentation .ppt /.pptx), PDF File .pdf), Text File .txt) or view presentation slides online. Scribd. Domain Driven Data Mining by Longbing Cao, Philip S Yu, Chengqi Zhang starting at $ Domain Driven Data Mining has 2 available editions to buy at Half Price Books Marketplace.

If the data is complex and very domain-specific, the methodologies discussed in domain-driven data mining [14, 9] concerning data intelligence, domain intelligence, human intelligence.

Domain-Driven Data Mining: Challenges and Prospects Abstract: Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models.

The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms have been designed of which.

Domain driven data mining is a data mining methodology for discovering actionable knowledge and deliver actionable insights from complex data and behaviors in a complex environment.

It studies the corresponding foundations, frameworks, algorithms, models, architectures, and evaluation systems for actionable knowledge discovery. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data.

If the ideas presented here appeal to you, I highly recommend that you deepen your toolbox by reading the book Domain-Driven Design: Tackling Complexity in the Heart of Software, by Eric Evans.

More than simply the original introduction to DDD, it is a treasure trove of information by one of the industry's most seasoned software designers.

Publication Type: Book Citation:pp. 1 - Closed Access. Filename Description Size; : MB. What is Domain Driven Data Mining. Definition of Domain Driven Data Mining: Data mining methodologies and techniques that utilize domain-oriented social intelligence, target dependable, trustworthy and actionable knowledge for business decision making.

Domain Driven Data Mining (D3M) Abstract: In deploying data mining into the real-world business, we have to cater for business scenarios, organizational factors, user preferences and business needs. However, the current data mining algorithms and tools often stop at the delivery of patterns satisfying expected technical interestingness.

This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute.

Data Mining for Business Applications presents state-of-the-art data mining research and development related to methodologies, techniques, approaches and successful applications. The contributions of this book mark a paradigm shift from "data-centered pattern mining" to "domain-driven actionable knowledge discovery (AKD)" for next-generation KDD research and applications.

Pris: kr. Inbunden, Skickas inom vardagar. Köp Domain Driven Data Mining av Longbing Cao, Philip S Yu, Chengqi Zhang, Yanchang Zhao på This paper proposes a new methodology named domain-driven data mining (DDDM), aims to discovery interesting and actionable knowledge for real user needs, overcome the gap between academia and business.

DDDM integrates domain knowledge, expert experience, user interestingness, rule action ability and data into mining system. Data and distributed domain driven architecture convergence Domain oriented data decomposition and ownership. Eric Evans's book Domain-Driven Design has deeply influenced modern architectural thinking, and consequently the organizational modeling.

It has influenced the microservices architecture by decomposing the systems into distributed. Conventional data mining applications face serious difficulties in solving complex real-life business decision making problems when practically deployed.

This work in order to improve the operations in a collection of business domains aims to suggest solutions by reviewing and studying the latest methodological, technical, practical progresses and some cases studies of data mining via domain.

Web mining, ranking, recommendations, social networks, and privacy preservation. ˜ e domain chapters also have an applied ˝ avor. Appropriate for both introductory and advanced data mining courses, Data Mining: ˜ e Text-book balances mathematical details and intuition.

It contains the necessary mathematical details. This paper reports a domain ontology-driven approach to data mining on a medical database containing clinical data on patients undergoing treatment for chronic kidney disease.

Each record within the dataset is comprised of a large number (up to 96) of quantitative and qualitative metrics which represent the physiological state of a particular. The Big Blue Book. Domain-Driven Design, by Eric Evans, provides a broad framework for making design decisions and a vocabulary for discussing domain design.

It is a synthesis of widely accepted best practices along with the author’s own insights and experiences. Domain Driven Data Mining, Springer. Cao, L., Yu, P.S., Zhang, C., Zhao, Y. 1st Edition.,XIII, p., Hardcover ISBN: Indeed, domain-driven data mining had attracted significant attention in the literature.

In the prominent TKDE paper “Domain-driven data mining: Challenges and prospects” and the ICDM workshop on domain-driven data mining, both contributed by Dr.

Longbing Cao, thorough efforts focused on promoting actionable knowledge discovery in complex real-world decision making tasks. The book gives both theoretical and practical knowledge of all data mining topics. It also contains many integrated examples and figures.

Every important topic is presented into two chapters, beginning with basic concepts that provide the necessary background for learning each data mining technique, then it covers more complex concepts and algorithms. Based on experience and lessons learned from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as domain-driven data mining.

On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative. Introduction. Domain Driven Data Mining (DDDM, D3M) [1,4], or Domain-Driven Actionable Knowledge Discovery (AKD) [2,11], aims at building the next-generation data mining and analytics methodologies, techniques and tools that can discover and deliver knowledge and intelligence for decision action-taking, i.e., actionable knowledge and actionable intelligence [2].To provide a systematic analysis, an initial framework with central dimensions of domain-driven data mining research in HR is firstly elaborated and the method of identifying and reviewing contributions is depicted.

The results of the review are presented and implications for future research on domain-driven data mining in HRM are derived. Real-world data mining generally must consider and involve domain and business oriented factors such as human knowledge, constraints and business expectations.

This encourages the development of a domain driven methodology to strengthen data-centered pattern mining. This report presents a review of the ACM SIGKDD Workshop on Domain Driven Data Mining (DDDM).