Data Mining: An Overview from a Database Perspective <s> Summary <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> Summary <s> A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases. > <s> BIB002 </s> Data Mining: An Overview from a Database Perspective <s> Summary <s> Previous studies on mining association rules find rules at single concept level, however, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. In this study, a top-down progressive deepening method is developed for mining multiplelevel association rules from large transaction databases by extension of some existing association rule mining techniques. A group of variant algorithms are proposed based on the ways of sharing intermediate results, with the relative performance tested on different kinds of data. Relaxation of the rule conditions for finding “level-crossing” association rules is also discussed in the paper. <s> BIB003
Data mining is a fast expanding eld with many new research results reported and new systems or prototypes developed recently. Researchers and developers in many elds have contributed to the state-of-the-art of data mining  BIB001 . Therefore, it is a challenging task to provide a comprehensive overview of the data mining methods within a short article. This article is an attempt to provide a reasonably comprehensive survey, from a database researcher's point of view, on the data mining techniques developed recently. An overview of data mining and knowledge discovery, from some data mining and machine learning researchers, has been performed recently 69]. The major di erence of our survey from theirs is the focus of this survey is on the techniques developed by database researchers, with an emphasis on e cient methods for data mining in very large databases. A classi cation of the available data mining techniques is provided and a comparative study of such techniques has been presented. Based on the diversity of data mining methods and rich functionalities of data mining investigated so far, many data mining systems or prototypes have been developed recently, some of which have been successfully used for mining knowledge in large databases. Here we brie y introduce some data mining systems reported in recent conferences and journals. However, this introduction is by no means complete. Appendices are welcome, and a comprehensive overview of such systems is necessary. Quest is a data mining system developed at the IBM Almaden Research Center by Agrawal, et al. 6] , which discovers various kinds of knowledge in large databases, including association rules 7, 78, 79], sequential patterns 8], classi cation rules 59], pattern matching and analysis 5], etc. KEFIR is a knowledge discovery system developed at the GTE Labs by Piatetsky-Shapiro, et al.   for the analysis of health care data. SKICAT is a knowledge discovery system, developed at Jet Propulsion Laboratory, which automatically detects and classi es sky objects from image data resulting from a major astronomical sky survey. DBMiner is a relational data mining system developed at Simon Fraser University by Han, et al. BIB002 BIB003  , for mining multiple kinds of rules at multiple abstraction levels, including characteristic rules, discriminant rules, association rules, classi cation rules, etc. KnowledgeMiner is a data mining system, developed by Shen et al. 75] , which integrates data mining with deductive database techniques and using meta-rule to guide the data mining process. INLEN is a system, developed by Michalski, et al. 61] , which integrates multiple learning paradigms in data mining. Explora is a multipattern and multistrategy discovery assistant developed by Kl osgen 52]. IMACS is a data mining system developed at AT&T Laboratory by Brachman et al.  ], using sophisticated knowledge representation techniques. DataMine is system exploring interactive ad-hoc query-directed data mining, developed by Imielinski, et al. 45] . IDEA, developed at AT&T Laboratory by Selfridge, et al. 74] , performs interactive data explorations and analysis. There have been many other data mining systems reported by machine learning and statistics researchers. Moreover, data warehousing systems have been seeking data mining tools for further enhancement of data analysis capabilities, and it is likely to see a trend of integration of the techniques of data warehousing and data mining in the near future. Besides the work done by database researchers, there have been fruitful results on data mining and knowledge discovery reported in many others elds. For example, researchers in statistics have developed many techniques which may bene t data mining 26]. Inductive logic programming 25] is a fast growing sub eld in logic programming which is closely related to data mining. There have also been active studies on data mining using data visualization techniques 49] and visualization of data mining results 51]. For lack of space, the results and issues in these related elds cannot be presented in this short article. An overview of data mining and knowledge discovery in a broader spectrum is left as a future exercise. As a young and promising eld, data mining still faces many challenges and unsolved problems which pose new research issues for further study. For example, further development of e cient methods for mining multiple kinds of knowledge at multiple abstraction levels, a exible and convenient data mining language or interface, the development of data mining techniques in advanced database systems, such as active databases, object-oriented databases, spatial databases, and multimedia databases, data mining in Internet information systems, the application of discovered knowledge, the integration of discovered and expert-de ned knowledge, and the method to ensure security and protection of privacy in data mining, are important issues for further study.