Data Mining: An Overview from a Database Perspective <s> Introduction <s> From the Publisher: ::: Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets. ::: The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called "discovery in databases." This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search. ::: Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in. ::: Gregory Piatetski-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTELaboratories. William Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project. <s> BIB001 </s> Data Mining: An Overview from a Database Perspective <s> Introduction <s> In May, 1995 an NSF workshop on the future of database management systems research was convened. This paper reports the conclusions of that meeting. Among the most important directions for future DBMS research recommended by the panel are: support for multimedia objects; managing distributed and loosely coupled information, as on the world-wide web; supporting new database applications such as data mining and warehousing; workflow and other complex transaction-management problems, and enhancing the ease-of-use of DBMS''s for both users and system managers. <s> BIB002
Recently, our capabilities of both generating and collecting data have been increasing rapidly. The widespread use of bar codes for most commercial products, the computerization of many business and government transactions, and the advances in data collection tools have provided us with huge amounts of data. Millions of databases have been used in business management, government administration, scienti c and engineering data management, and many other applications. It is noted that the number of such databases keeps growing rapidly because of the availability of powerful and a ordable database systems. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance  BIB001 BIB002 . Data mining, which is also referred to as knowledge discovery in databases, means a process of nontrivial extraction of implicit, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases 70] . There are also many other terms, appearing in some articles and documents, carrying a similar or slightly di erent meaning, such as knowledge mining from databases, knowledge extraction, data archaeology, data dredging, data analysis, and so on. By knowledge discovery in databases, interesting knowledge, regularities, or high-level information can be extracted from the relevant sets of data in databases and be investigated from di erent angles, and large databases thereby serve as rich and reliable sources for knowledge generation and veri cation. Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. The discovered knowledge can be applied to information management, query processing, decision making, process control, and many other applications. Researchers in many di erent elds, including database systems, knowledge-base systems, arti cial intelligence, machine learning, knowledge acquisition, statistics, spatial databases, and data visualization, have shown great interest in data mining. Furthermore, several emerging applications in information providing services, such as on-line services and World Wide Web, also call for various data mining techniques to better understand user behavior, to meliorate the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey on the data mining techniques developed in several research communities, with an emphasis on database-oriented techniques and those implemented in applicative data mining systems. A classi cation of the available data mining techniques is also provided, based on the kinds of databases to be mined, the kinds of knowledge to be discovered, and the kinds of techniques to be adopted. This survey is organized according to one classi cation scheme: the kinds of knowledge to be mined.