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56
Data Mining: An Overview from Database Perspective
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 1996
"... 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. Researchers in many different fields have sh ..."
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Cited by 532 (26 self)
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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. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided and a comparative study of such techniques is presented.
A General Incremental Technique for Maintaining Discovered Association Rules
- In Proceedings of the Fifth International Conference On Database Systems For Advanced Applications
, 1997
"... A more general incremental updating technique is developed for maintaining the association rules discovered in a database in the cases including insertion, deletion, and modification of transactions in the database. A previously proposed algorithm FUP can only handle the maintenance problem in the c ..."
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Cited by 110 (5 self)
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A more general incremental updating technique is developed for maintaining the association rules discovered in a database in the cases including insertion, deletion, and modification of transactions in the database. A previously proposed algorithm FUP can only handle the maintenance problem in the case of insertion. The proposed algorithm FUP2 makes use of the previous mining result to cut down the cost of finding the new rules in an updated database. In the insertion only case, FUP2 is equivalent to FUP. In the deletion only case, FUP2 is a complementary algorithm of FUP which is very efficient when the deleted transactions is a small part of the database, which is the most applicable case. In the general case, FUP2 can efficiently update the discovered rules when new transactions are added to a transaction database, and obsolete transactions are removed from it. The proposed algorithm has been implemented and its performance is studied and compared with the best algorithms for mining...
The role of Occam’s Razor in knowledge discovery
- Data Mining and Knowledge Discovery
, 1999
"... Abstract. Many KDD systems incorporate an implicit or explicit preference for simpler models, but this use of “Occam’s razor ” has been strongly criticized by several authors (e.g., Schaffer, 1993; Webb, 1996). This controversy arises partly because Occam’s razor has been interpreted in two quite di ..."
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Cited by 104 (3 self)
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Abstract. Many KDD systems incorporate an implicit or explicit preference for simpler models, but this use of “Occam’s razor ” has been strongly criticized by several authors (e.g., Schaffer, 1993; Webb, 1996). This controversy arises partly because Occam’s razor has been interpreted in two quite different ways. The first interpretation (simplicity is a goal in itself) is essentially correct, but is at heart a preference for more comprehensible models. The second interpretation (simplicity leads to greater accuracy) is much more problematic. A critical review of the theoretical arguments for and against it shows that it is unfounded as a universal principle, and demonstrably false. A review of empirical evidence shows that it also fails as a practical heuristic. This article argues that its continued use in KDD risks causing significant opportunities to be missed, and should therefore be restricted to the comparatively few applications where it is appropriate. The article proposes and reviews the use of domain constraints as an alternative for avoiding overfitting, and examines possible methods for handling the accuracy–comprehensibility trade-off.
Constructing Knowledge From Multivariate Spatiotemporal Data: Integrating Geographic Visualization (GVis) with Knowledge Discovery in Database (KDD) Methods
- International Journal of Geographical Information Science
, 1999
"... In this paper, we develop an approach to the process of constructing knowledge through structured exploration of large spatiotemporal data sets. We begin by introducing our problem context and defining both Geographic Visualization (GVis) and Knowledge Discovery in Databases (KDD), the source domain ..."
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Cited by 85 (20 self)
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In this paper, we develop an approach to the process of constructing knowledge through structured exploration of large spatiotemporal data sets. We begin by introducing our problem context and defining both Geographic Visualization (GVis) and Knowledge Discovery in Databases (KDD), the source domains for methods being integrated. Next, we review and compare recent GVis and KDD developments and consider the potential for their integration, emphasizing that an iterative process with user interaction is a central focus for uncovering interesting and meaningful patterns through each. We then introduce an approach to design of an integrated GVis-KDD environment directed to exploration and discovery in the context of spatiotemporal environmental data. The approach emphasizes a matching of GVis and KDD meta-operations. Following description of the GVis and KDD methods that are linked in our prototype system, we present a demonstration of the prototype applied to a typical spatiotemporal datas...
GeoMiner: A System Prototype for Spatial Data Mining
, 1997
"... Spatial data mining is to mine high-level spatial information and knowledge from large spatial databases. A spatial data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational data mining system, DBMiner, and ..."
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Cited by 77 (3 self)
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Spatial data mining is to mine high-level spatial information and knowledge from large spatial databases. A spatial data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational data mining system, DBMiner, and our research into spatial data mining. The data mining power of GeoMiner includes mining three kinds of rules: characteristic rules, comparison rules, and association rules, in geo-spatial databases, with a planned extension to include mining classification rules and clustering rules. The SAND (Spatial And Nonspatial Data) architecture is applied in the modeling of spatial databases, whereas GeoMiner includes the spatial data cube construction module, spatial on-line analytical processing (OLAP) module, and spatial data mining modules. A spatial data mining language, GMQL (Geo- Mining Query Language), is designed and implemented as an extension to Spatial SQL [3], for spatial data mini...
A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction.
- Stanford University
"... This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by su ..."
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Cited by 47 (5 self)
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This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization. The paper also proposes some genetic operators tailored for the two above data mining tasks. 1. Introduction Data Mining (DM) consists of the extraction of interesting, novel knowledge from real-world databases [Fayyad et al. 96]. DM is an interdisciplinary subject, whose core lies at the intersection of machine learning and databases. Four desirable characteristics of a DM system are: (1) the discovery of comprehensible knowledge, typically expressed by high-level rules; (2) integration with databases [Ha...
Generalization and Decision Tree Induction: Efficient Classification In Data Mining
- IN PROC. OF 1997 INT. WORKSHOP RESEARCH ISSUES ON DATA ENGINEERING (RIDE'97
, 1997
"... Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. T ..."
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Cited by 35 (4 self)
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Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. This paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute-oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient, high-quality, multiple-level classification of large amounts of data, the relaxation of the requirement of perfect training sets, and the elegant handling of continuous and noisy data.
DB Miner: A System for Data Mining in Relational Databases and Data Warehouses.
- Proceedings of the 1997 Conference of the Centre for Advanced Studies on Collaborative research.
, 1997
"... ..."
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A Survey of Methods for Scaling Up Inductive Learning Algorithms
, 1997
"... Each year, one of the explicit challenges for the KDD research community is to develop methods that facilitate the use of inductive learning algorithms for mining very large databases. By collecting, categorizing, and summarizing past work on scaling up inductive learning algorithms, this paper serv ..."
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Cited by 20 (2 self)
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Each year, one of the explicit challenges for the KDD research community is to develop methods that facilitate the use of inductive learning algorithms for mining very large databases. By collecting, categorizing, and summarizing past work on scaling up inductive learning algorithms, this paper serves to establish a common ground for researchers addressing the challenge. We begin with a discussion of important, but often tacit, issues related to scaling up learning algorithms. We highlight similarities among methods by categorizing them into three main approaches. For each approach, we then describe, compare, and contrast the different constituent methods, drawing on specific examples from the published literature. Finally, we use the preceding analysis to suggest how one should proceed when dealing with a large problem, and where future research efforts should be focused.
Parallel ROLAP Data Cube Construction on Shared-Nothing Multiprocessors
, 2003
"... The pre-computation of data cubes is critical to improving the response time of On-Line Analytical Processing (OLAP) systems and can be instrumental in accelerating data mining tasks in large data warehouses. In order to meet the need for improved performance created by growing data sizes, parallel ..."
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Cited by 19 (11 self)
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The pre-computation of data cubes is critical to improving the response time of On-Line Analytical Processing (OLAP) systems and can be instrumental in accelerating data mining tasks in large data warehouses. In order to meet the need for improved performance created by growing data sizes, parallel solutions for generating the data cube are becoming increasingly important. This paper presents a parallel method for generating data cubes on a sharednothing multiprocessor. Since no (expensive) shared disk is required, our method can be used on low cost Beowulf style clusters consisting of standard PCs with local disks connected via a data switch. Our approach uses a ROLAP representation of the data cube where views are stored as relational tables. This allows for tight integration with current relational database technology.
