knowledge discovery in databases

Introduction

With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important to develop powerful means for analysis and interpretation of such data and for the extraction of interesting knowledge that could help in decision-making. 
Data Mining, also popularly known as Knowledge Discovery in Databases (KDD), refers to the extraction of previously unknown and potentially usefull information from data in databases. While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process.
The KDD is an iterative process. Once the discovered knowledge is presented to the user, the evaluation measures can be enhanced, the mining can be further refined, new data can be selected or further transformed, or new data sources can be integrated, in order to get different, more appropriate results.
The goal of this paper is to examine some types of techniques for minig data as well as the related algorithms.