Data Mining by Mehmed Kantardzic (good book recommendations TXT) 📕

http://jmlr.csail.mit.edu

The JMLR provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR provides a venue for papers on machine learning featuring new algorithms with empirical, theoretical, psychological, or biological justification; experimental and/or theoretical studies yielding new insights into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical-learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.

7.ACM Transactions on Knowledge Discovery from Data (TKDD)

http://tkdd.cs.uiuc.edu/index.html

The ACM TKDD addresses a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include scalable and effective algorithms for data mining and data warehousing, mining data streams, mining multimedia data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social-network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, preprocessing and postprocessing for data mining, robust and scalable statistical methods, data-mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data-mining technology.

8.Journal of Intelligent Information Systems (JIIS)

http://www.springerlink.com/content/0925-9902

The JIIS: Integrating Artificial Intelligence and Database Technologies fosters and presents research and development results focused on the integration of AI and database technologies to create next generation information systems—intelligent information systems. JIIS provides a forum wherein academics, researchers, and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. Articles published in JIIS include research papers, invited papers, meeting, workshop and conference announcements and reports, survey and tutorial articles, and book reviews. Topics include foundations and principles of data, information, and knowledge models; and methodologies for IIS analysis, design, implementation, validation, maintenance and evolution.

9.Statistical Analysis and Data Mining

http://www.amstat.org/publications/sadm.cfm

The Statistical Analysis and Data Mining addresses the broad area of data analysis, including data-mining algorithms, statistical approaches, and practical applications. Topics include problems involving massive and complex data sets, solutions using innovative data-mining algorithms and/or novel statistical approaches, and the objective evaluation of analyses and solutions. Of special interest are articles that describe analytical techniques and discuss their application to real problems in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce.

10.Intelligent Data Analysis

http://www.iospress.nl/html/1088467x.php

Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of AI techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to) all areas of data visualization, data preprocessing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and postprocessing. In particular, we prefer papers that discuss development of new AI-related data analysis architectures, methodologies, and techniques and their applications to various domains. Papers published in this journal are geared heavily toward applications, with an anticipated split of 70% of the papers published being application-oriented research, and the remaining 30% containing more theoretical research.

A.2 DATA-MINING CONFERENCES

1.SIAM International Conference on Data Mining, SDM

http://www.siam.org/meetings/

This conference provides a venue for researchers who are addressing extracting knowledge from large datasets that requires the use of sophisticated, high-performance and principled analysis techniques and algorithms, based on sound theoretical and statistical foundations. It also provides an ideal setting for graduate students and others new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending presentations and tutorials (included with conference registration). A set of focused workshops are also held in the conference. The proceedings of the conference are published in archival form, and are also made available on the SIAM Web site.

2.The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

http://sigkdd.org/conferences.php

The annual ACM SIGKDD conference is the premier international forum for data-mining researchers and practitioners from academia, industry, and government to share their ideas, research results, and experiences. It features keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, and demonstrations. Authors can submit their original work either to SIGKDD Research track or SIGKDD Industry/Government track. The research track accepts papers on all aspects of knowledge discovery and data mining overlapping with topics from machine learning, statistics, databases, and pattern recognition. Papers are expected to describe innovative ideas and solutions that are rigorously evaluated and well presented. The Industrial/Government track highlights challenges, lessons, concerns, and research issues arising out of deploying applications of KDD technology. The focus is on promoting the exchange of ideas between researchers and practitioners of data mining.

3.IEEE International Conference on Data Mining (ICDM)

http://www.cs.uvm.edu/∼icdm/

The IEEE ICDM has established itself as the world’s premier research conference in data mining. The conference provides a leading forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. In addition, ICDM draws researchers and application developers from a wide range of data mining-related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high-quality research findings, and innovative solutions to challenging data-mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference will feature workshops, tutorials, panels, and the ICDM data-mining contest.

4.International Conference on Machine Learning and Applications (ICMLA)

http://www.icmla-conference.org/

The aim of the conference is to bring researchers working in the areas of machine learning and applications together. The conference will cover