CiteSeerX — Search Results — knowledge discovery

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Knowledge Discovery in Databases: an Overview

by William J. Frawley, Gregory Piatetsky-shapiro, Christopher J. Matheus , 1992

"... this article. 0738-4602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990) ..."

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this article. 0738-4602/92/$4.00 1992 AAAI 58 AI MAGAZINE for the 1990s (Silberschatz, Stonebraker, and Ullman 1990)

From Data Mining to Knowledge Discovery in Databases.

by Usama Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth - AI Magazine, , 1996

"... ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in database ..."

Abstract - Cited by 538 (0 self) - Add to MetaCart

■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery

Systems for Knowledge Discovery in Databases

by Christopher J. Matheus, Philip K. Chan, Gregory Piatetsky-shapiro - IEEE Transactions On Knowledge And Data Engineering , 1993

"... The automated discovery of knowledge in databases is becoming increasingly important as the world's wealth of data continues to grow exponentially. Knowledge-discovery systems face challenging problems from real-world databases which tend to be dynamic, incomplete, redundant, noisy, sparse, and ..."

Abstract - Cited by 118 (8 self) - Add to MetaCart

The automated discovery of knowledge in databases is becoming increasingly important as the world's wealth of data continues to grow exponentially. Knowledge-discovery systems face challenging problems from real-world databases which tend to be dynamic, incomplete, redundant, noisy, sparse

Knowledge Discovery

by Claudia Plant, Christian Böhm, Technische Universität München, Ludwig Maximilians Universität München

"... Clustering or finding a natural grouping of a data set is essential for knowledge discovery in many applications. This chapter provides an overview on emerging trends within the vital research area of clustering including subspace and projected clustering, correlation clustering, semi-supervised clu ..."

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Clustering or finding a natural grouping of a data set is essential for knowledge discovery in many applications. This chapter provides an overview on emerging trends within the vital research area of clustering including subspace and projected clustering, correlation clustering, semi

What makes patterns interesting in knowledge discovery systems

by Avi Silberschatz, Alexander Tuzhilin - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 1996

"... One of the central problems in the eld of knowledge discovery is the development ofgood measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures -- those that depend only on the structure of a pattern and the underlying data used in the ..."

Abstract - Cited by 276 (13 self) - Add to MetaCart

One of the central problems in the eld of knowledge discovery is the development ofgood measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures -- those that depend only on the structure of a pattern and the underlying data used

Levelwise Search and Borders of Theories in Knowledge Discovery

by Heikki Mannila, Hannu Toivonen , 1997

"... One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm fo ..."

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One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm