Piatetsky--Shapiro, G. and Frawley, W.J. (Editors), 1991: Knowledge discovery in databases.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... alarm correlation see [18] Similar approaches have been used successfully also in process control tasks [9] For a recent survey about fault management in communication networks, see [10] Our approach andm ethods draw from the eldof Knowledge Discovery and Data Mining (KDD) for overviews, see [11, 12]. KDD can be loosely de ned as the task of obtaining useful and interesting knowledge from large collections of data. It combines methods and tools from machine learning, statistics, and databases. A related approach for the automatic acquisition of network manage mentknowledge from the ....

G. Piatetsky - Shap iroand W.J.Frawley (eds.), Knowledge Discovery in Databases ,AAAI Press, Menlo Park, California.

.... 0003 contract number DAAH04 95 C 0008, and by the National Science Foundation under grant 9631539. 1 Introduction Data mining is a process to extract nontrivial, previously unknown and potentially useful information (such as knowledge rules, constraints, regularities) from data in databases [11, 4]. The explosive growth in data and databases used in business management, government administration, and scientific data analysis has created a need for tools that can automatically transform the processed data into useful information and knowledge. Spatial data mining is a process of discovering ....

G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.

....errors in annotation of function, Brenner, 1999) both of which add noise to the data mining process (Mitchell, 1997) 2) Describe each ORF in the genome using a defined language the descriptions are based solely on information which can be computed from the sequence. 3) Use data mining (Piatetsky Shapiro and Frawley, 1991; Fayyad et al. 1996; Munakata, 1999) to identify frequent patterns in the descriptions of the ORFs of assigned function ORFs of unassigned function STR SEQ SIM Datalog Databases Data Mining 2 3 1 3 2 3 1 3 Databases for rule learning Method Validation Experimental Confirmation ....

Piatetsky-Shapiro,G. and Frawley,W. (1991) Knowledge Discovery in Databases. MIT Press, Boston, MA.

....branch of computer science concerned with the automatic extraction from data of implicit and previously unknown information which is nontrivial, understandable, and useful, using techniques from Machine Learning and Statistical Pattern Recognition (clustering, rule induction, classification, etc. (Piatetsky Shapiro Frawley 1991). Domain Database Knowledge Incomplete Noisy Overgeneralization Combinatorial Explosion Knowledge Representation Scheme Figure 1: The process of data mining Data Mining (Figure 1) presupposes a database (or several databases) containing data from a particular domain. The collected data ....

Piatetsky-Shapiro, G. & Frawley, W. 1991. Knowledge discovery in databases. AAAI Press.

....of fuzzy sets is categorized below based on the di#erent functions of data mining that are modeled. A. 1 Clustering Data mining aims at sifting through large volumes of data in order to reveal useful information in the form of new relationships, patterns, or clusters, for decision making by a user [60]. Fuzzy sets support a focused search, specified in linguistic terms, through data. They also help discover dependencies between the data in qualitative semiqualitative format. In data mining, one is typically interested in a focused discovery of structure and an eventual quantification of ....

P. Piatetsky-Shapiro and W. J. Frawley, eds., Knowledge Discovery in Databases. Menlo Park, CA: AAAI/MIT Press, 1991.

....visualize temporal relationships, a parallel coordinate graph for displaying the temporal relationships has been developed. 1 Introduction In recent years data mining has emerged as a field of investigation concerned with automating the process of finding patterns within large volumes of data [9]. The results of data mining are often complex in their own right and visualisation has been widely employed as a technique for assisting users in seeing the underlying semantics [12] In addition, mining from temporal data has received increased attention recently as it provides insight into the ....

Piatetsky-Shapiro, G. and W. Frawley, J., Eds. Knowledge Discovery in Databases. Menlo park, California, AAAI Press, (1991).

.... items, clustering, linguistic terms TUCS Research Group Algorithmics Research Group 1 Introduction Data mining, sometimes referred to as knowledge discovery in databases, is concerned with the nontrivial extraction of implicit, previously unknown, and potentially useful information from data [1]. Mining association rules is one of the important research problems in data mining. An interesting association rule describes an interesting relationship among different attributes. The problem of mining boolean association rules over basket data was introduced in [2] and later broadened in [3] ....

Piatetsky-Shapiro, G., Frawley, W.J.: Knowledge Discovery in Databases. AAAI Press/The MIT Press, Menlo Park, California (1991)

....number DAAH04 95 C 0008, and by the National Science Foundation under grant 9631539. 1 Introduction Data mining is a process to extract implicit, nontrivial, previously unknown and potentially useful information(such as knowledge rules, constraints, regularities) from data in databases [12, 4]. The explosive growth in data and databases used in business management, government administration, and scientific data analysis has created a need for tools that can automatically transform the processed data into useful information and knowledge. Spatial data mining is a process of discovering ....

G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.

....corporate data sets basically remain relatively stable, at least in a period of time, although the amount of data keeps increasing exponentially. Data mining applications undoubtedly have great potentials for nding interesting, nontrivial, potentially valuable patterns over these large data sets [9,16]. However, it is www.elsevier.com locate ida Intelligent Data Analysis 3 (1999) 211225 Corresponding author. Tel. 1 301 441 1498; fax: 1 301 441 1498; e mail: zfu rhsmith.umd.edu 1088 467X 99 see front matter # 1999 Published by Elsevier Science B.V. All rights reserved. PII: ....

....size [8] However, in most real world applications, the domain knowledge is not a priori. It is also not known what the best subset is and what should be contained in such a subset. Numerous algorithms and approaches have been previously developed for data reduction, from articial intelligence [1,16,17], data mining [9] as well as statistical community [2] What data mining really needs to do is to eciently nd the optimal subset of very large data sets from both variable and observation perspectives. We could then interpret the underlying data structures, and reliably dig out the nontrivial, ....

G. Piatetsky-Shapiro, W. Frawley, Knowledge discovery in databases, MIT Press, Cambridge, MA, 1991.

....properties, opening up the feasibility of mining association rules over very large databases. The problem of finding association rules falls within the purview of database mining [AIS93a] ABN92] HS94] MKKR92] S 93] Tsu90] also called knowledge discovery in databases [HCC92] Lub89] PS91b] Related, but not directly applicable, work includes the induction of classification rules [BFOS84] Cat91] FWD93] HCC92] Qui93] discovery of causal rules [CH92] Pea92] learning of logical definitions [MF92] Qui90] fitting of functions to data [LSBZ87] Sch90] and clustering [ANB92] C ....

G. Piatestsky-Shapiro, editor. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.

.... also been reported (Compton, Edwards, Kang, Malor, Menzies, Preston, Srinivasan and Sammut, 1991) 2 Another approach to the development and maintenance of large rule based systems has been the use of inductive modeling techniques to analyze large databases and develop knowledge based systems (Piatetsky Shapiro and Frawley, 1991). With the increasing power of modern workstations and improvements in inductive techniques it is possible to develop inductive models of databases with tens of thousands of cases in a few minutes. It is possible to combine both approaches by developing an algorithm for the induction of ....

Piatetsky-Shapiro, G. and Frawley, W., Ed. (1991). Knowledge Discovery in Databases. Cambridge, Massachusetts, MIT Press.

....by low level primitives. The discovery of clear and concise relationships or regularities among the collected data, can be handled by a knowledge discovery technique which performs efficient and effective data generalization to discover useful knowledge or regularity from the collected information [11, 12]. Third, process control and system management in a dynamic environment often require prompt, real time, and intelligent reactions in response to situation changes in the environment. These reactions can be dealt with by application of active database technology[1] for automatic and prompt ....

G. Piatetsky-Shapiro and W.J. Frawley, Knowledge Discovery in Databases, AAAI/MIT Press, 1991.

....a common everyday routine. The data thus collected has grown exponentially and is now impossible for any human to intelligently analyse them. This brought forward an interesting problem which is known as database mining, knowledge discovery in databases, data archeology, data dredging, etc. [42, 30, 15]. Knowledge discovery in spatial databases, or spatial data mining, can be defined as the discovery of interesting, implicit, and previously unknown knowledge [19] from large spatial databases. Data mining in general has become a cross product of different fields of research including machine ....

....With the advent of data mining, reserachers proposed various methods for discovering knowledge from large databases, almost all of them concentrating on relational databases. These methods strived to combine the already mature areas like machine learning, databases and statistics. Studies like [42, 26, 1, 15] lay a foundation for spatial data mining. The study by Lu et al. 36] is probably the first one to extend data mining techniques to spatial databases. Machine learning techniqes learning from examples and generalization and specialization are widely used in such algorithms. It did not take long ....

G. Piatetsky-Shapiro and W. J. Frawley, editors. Knowledge Discovery in Databases. AAAI/MIT Press, Menlo Park, CA, 1991.

....learning applications in agriculture, and describe a first generation tool that we have built to aid in the recording and use of metadata in database mining. 1. Introduction Database mining is the process of discovering previously unknown and potentially interesting patterns in large databases [1]. The extracted information is typically organized into a prediction or classification model of the data. Techniques for finding the interesting patterns range from applications of computational models such as machine learning, fuzzy logic and artificial neural networks to statistical models such ....

Piatetsky-Shapiro, G., and Frawley, W. J., eds. (1991) Knowledge Discovery in Databases. Menlo Park, CA: AAAI Press.

....SPAIN International Journal of Approximate Reasoning 1994 11:1 158 c fl 1994 Elsevier Science Inc. 655 Avenue of the Americas, New York, NY 10010 0888 613X 94 7.00 1 2 1. LEARNING CAUSAL NETWORKS: THE POSSIBILISTIC CASE As more and more databases are used as a source for Knowledge Discovery [38], the interest of automating the construction of a well defined and useful knowledge representation as belief networks [35, 34, 33] becomes apparent. Several methods have been devised to recover both the structure and the probability distributions corresponding to it. Such methods can be roughly ....

G. Piatetsky-Shapiro and W.J. Frawley, editors. Knowledge Discovery in Databases. AAAI Press. Menlo Park, Ca, 1991.

....induction, knowledge acquisition Keywords Phrases: data mining, parallel databases, knowledge discovery in databases 1. Introduction In recent years, the scientific community has shown a rapidly growing interest in the discovery of hidden information in databases, also known as Data Mining [5, 8, 14]. The motivation for Data Mining is that databases contain more information than the stored facts themselves. For example, in commercial databases, global changes over time (trends) or (unknown) dependencies among attributes provide a valuable source of higher level strategic information. ....

Gregory Piatetsky-Shapiro and William J. Frawley, editors. Knowledge Discovery in Databases. AAAI Press, Menlo Park, California, 1991.

....for geographic data, OGIS [30] need to address these issues. 3 2. 3 Data mining and Knowledge Discovery Data mining is a process to extract implicit, nontrivial, previously unknown and potentially useful information(such as knowledge rules, constraints, regularities) from data in databases [32]. The explosive growth in data and databases used in business management, government administration, and scientific data analysis has created a need for tools that can automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research ....

G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991.

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Piatetsky-Shapiro G. Discovery, Analysis and Presentation of Strong Rules. Knowledge Discovery in Databases, 1991.

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Piatetsky--Shapiro, G. and Frawley, W.J. (Editors), 1991: Knowledge discovery in databases.

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G. Piatetsky-Shapiro and W. Frawley, Knowledge Discovery in Databases, AAAI Press, Menlo Park, Calif., 1991.

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Piatetsky-Shapiro G., "Knowledge Discovery in Databases", AAAI Press, distributed by MIT Press, 1991.

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Piatetsky-Shapiro, G., Frawley W. (1991) Knowledge Discovery in Databases MIT Press.

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Piatetsky-Shapiro, G. and W. J. Frawley, editors. Knowledge Discovery in Databases. AAAI/MIT Press, Menlo Park, CA, 1991.

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Piatetsky-Shapiro, G. and W. J. Frawley. Knowledge Discovery in Databases. AAAI Press. Menlo Park, CA. 1991.

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Piatetsky-Shapiro, Gregory, and Frawley, William J., eds. (1991). Knowledge Discovery in Databases. Menlo Park, CA: AAAI Press.

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