Data Mining and Information Retrieval is an emerging interdisciplinary discipline dealing with Information Retrieval and Data Mining techniques. It has undergone rapid development with the advances in mathematics, statistics, information science, and computer science. In this paper, we present an empirical analysis of publication metadata obtained from 6 top-tier journals and 9 conferences for the first 16 years of the 21st Century, and evaluate the dynamic characteristics of Data Mining and Information Retrieval. We find a steady growth both in terms of productivity and impact, evidenced by the unabated number of publications/citations over the period of study. We note that the modality for co-operation in this field is changing from independent to collaborative. Furthermore, according to the citation pattern, the field is becoming open-minded as illustrated by a gradual decline of self-citation rates, which was dropped to 10% in 2015, nearly three times lower than what it was in 2000. Finally, we explore the inner structure relying on the topics evolution from the aspects of popular keywords/topics identification and evolution. Overall, this study provides insights of Data Mining and Information Retrieval behind its demonstrated growth in the recent past, with the ultimate goal of revealing its potential of driving scientific innovation in the future.

数据挖掘和信息检索是一门新兴的跨学科学科，涉及信息检索和数据挖掘技术。随着数学，统计学，信息科学和计算机科学的发展，它得到了快速的发展。在本文中，我们对21世纪前16年从6种顶级期刊和9个会议获得的出版物元数据进行了实证分析，并评估了数据挖掘和信息检索的动态特征。我们发现，在生产力和影响力方面，都有稳定的增长，这一点可以通过研究期间出版/引用的数量不减而得到证明。我们注意到，该领域的合作方式正在从独立变为合作。此外，根据引用方式，自引用率逐渐下降就说明了这个领域的思想开放，自引用率在2015年下降到10％，比2000年下降了近三倍。最后，我们根据主题探索内部结构从热门关键字/主题的识别和演化方面发展。总体而言，本研究提供了数据挖掘和信息检索的最新见解，其背后是近来已证明的增长，其最终目标是揭示其在未来推动科学创新的潜力。