The Second International Conference on Digital Information and Communication Technology and its Applications (DICTAP2012)


ADVANCED DATA MINING TECHNIQUES (Special Session)

Recent progress in scientific and engineering applications has accumulated huge volumes of high-dimensional data, stream data, and spatial and temporal data. Highly scalable and sophisticated data mining tools for such applications represent are of the most active research frontiers in data mining. Emerging applications like stream data, moving object data, RFID data, data from sensor networks, multi-agent data , semantic web, web search, biomedical engineering, telecommunications, geospatial data , climate data and Earth’s ecosystems etc. involve great management challenges that also represent new opportunities for data mining research. The last few decades have witnessed an unprecedented explosion of data in almost all fields, ranging from anthropology to astronomy. Knowledge discovery (KD) technology empowers development of the next generation database management and information systems through its abilities to extract new, insightful information embedded within large heterogeneous databases and to formulate knowledge. A KD process includes “data warehousing, target data selection, cleaning, preprocessing, transformation and reduction, data mining, model selection, evaluation and interpretation, and finally consolidation and use of the extracted knowledge. Specifically, data mining aims to develop algorithms for extracting new patterns from the facts recorded in a database. Hitherto, data mining tools adopted techniques from statistics, neural network modeling, and visualization to classify data and identify patterns.

In light of the tremendous amount of fast-growing and sophisticated types of data and comprehensive data analysis tasks, data mining technology may be only in its infancy, as the technology is still far from adequate for handling the large-scale and complex emerging application problems. Research is needed to develop highly automated, scalable, integrated reliable data mining systems and tools. Moreover, it is important to promote information exchange among users, data analysts, system developers, and data mining researchers to facilitate the advances available from data mining research, application development and technology transfer.

Original manuscripts that enhance the level of research and contribute new developments to the data mining sector are encouraged. The submission will be peer-reviewed by specialized referees. Prospective authors are invited to submit their research papers on the topics, but not limited to the following.

Graph mining
Data mining in bioinformatics
Privacy-aware data mining
Large scale data mining
Temporal pattern mining
Stream data mining
Mining moving object data, RFID data, and data from sensor networks
Ubiquitous knowledge discovery
Mining multi-agent data
Mining and link analysis in networked settings: web, social and computer networks, and online communities
Mining the semantic web
Data mining in electronic commerce
Web search, advertising, and marketing task

Submission of papers
Authors are invited to submit their research papers through conference management system and also send through email to the chair avsenthilkumar@gmail.com

The proceedings of the special session will be published by CCIS, Series of LNCS Springer (Indexed by Scopus, EI and others).

Session Organizer
Dr.A.V.Senthil Kumar
Director, Department of MCA
Hindusthan College of Arts and Science
Coimbatore -28, Tamilnadu. India