Data Mining Research Papers - Academia.edu.
Educational data mining (EDM) is a research area which utilizes data mining techniques and research approaches for understanding how students learn. Interactive e-learning methods and tools have opened up opportunities to collect and scrutinize student data, to ascertain patterns and trends in those data, and to formulate new discoveries and test assumptions about how students learn.
Original research papers, state-of-the-art reviews, and technical notes are invited for publication. The journal accepts paper submission of any work relevant to data warehousing and data mining with special attention to papers focusing on mining of data from data warehouses, integration of databases, data warehousing, data mining, and holistic approaches to mining and archiving data.
Applying data mining DM in education is an emerging interdisciplinary research field also known as educational data mining EDM. It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain.
Overview. The Data Platforms and Analytics pillar currently consists of the Data Management, Mining and Exploration Group (DMX) group, which focuses on solving key problems in information management. Our current areas of focus are infrastructure for large-scale cloud database systems, reducing the total cost of ownership of information management, enabling flexible ways to query, browse and.
The two-volume set LNAI 6634 and 6635 constitutes the refereed proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, held in Shenzhen, China in May 2011. The total of 32 revised full papers and 58 revised short papers were carefully reviewed and selected from 331 submissions.
Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and implicit spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this paper, we explore the emerging field of spatial data mining, focusing on.
Conference papers of each Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining are published through ACM. KDD is widely considered the most influential forum for knowledge discovery and data mining research.