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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Data Mining Problems and Solutions for Response Modeling in CRM
"... This paper presents three data mining problems that are often encountered in building a response model. They are robust modeling, variable selection and data selection. Respective algorithmic solutions are given. They are bagging based ensemble, genetic algorithm based wrapper approach and nearest n ..."
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This paper presents three data mining problems that are often encountered in building a response model. They are robust modeling, variable selection and data selection. Respective algorithmic solutions are given. They are bagging based ensemble, genetic algorithm based wrapper approach and nearest
KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems ⋆
"... be inserted by the editor) ..."
System Optimisation by Fusion of Information for Data Mining Problems
"... This paper presents a new fusion model called System Optimisation by Fusion of Information (SOFI) which is derived from Bayesian networks. It is designed to improve user decision-making through the help of the more active and powerful Decision Support System (DSS) that is used to reduce the “Informa ..."
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the “Information Gap ” for data mining applications. The SOFI module is a software which can be integrated in every kind of data mining platform that gathers several data mining tools. It is especially designed for the Knowledge Discovery in Databases (KDD). After accumulating various analyses, the user has quick
Constrained optimization of data-mining problems to improve model performance: A direct-marketing application
, 2005
"... D/2005/7012/16 Constrained optimization of data-mining problems to improve model performance: A direct-marketing application ..."
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Cited by 9 (0 self)
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D/2005/7012/16 Constrained optimization of data-mining problems to improve model performance: A direct-marketing application
From Data Mining to Knowledge Discovery in Databases.
- 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 ..."
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Cited by 538 (0 self)
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■ 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
Mining Sequential Patterns
, 1995
"... We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empiri ..."
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Cited by 1568 (6 self)
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We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem
Privacy-Preserving Data Mining
, 2000
"... A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models with ..."
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Cited by 844 (3 self)
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A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models
Privacy Preserving Data Mining
- JOURNAL OF CRYPTOLOGY
, 2000
"... In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated b ..."
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Cited by 525 (9 self)
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In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated
Data Mining: Concepts and Techniques
, 2000
"... Our capabilities of both generating and collecting data have been increasing rapidly in the last several decades. Contributing factors include the widespread use of bar codes for most commercial products, the computerization of many business, scientific and government transactions and managements, a ..."
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Cited by 3142 (23 self)
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of data and information. This explosive growth in stored data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. This book explores the concepts and techniques of data mining
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