Publisher's Synopsis
The extensive use of computers and information technology has led toward the creation of extensive data repositories from a very wide variety of application areas. Such vast data repositories can contribute significantly towards future decision making provided appropriate knowledge discovery mechanisms are applied for extracting hidden, but potentially useful information embedded into the data. Data mining (DM) is one of the phases in knowledge discovery in databases. It is the process of extracting the useful information and knowledge in which the data is abundant, incomplete, ambiguous and random. DM is defined as an automated or semi-automated exploratory data analysis of large complex data sets that can be used to uncover patterns and relationships in data with an emphasis on large observational databases. Modern statistical and computational technologies are applied to the problem in order to find useful patterns hidden within a large database. To uncover hidden trends and patterns, DM uses a combination of an explicit knowledge base, sophisticated analytical skills, and domain knowledge. In effect, the predictive models formed from the trends and patterns through DM enable analysts to produce new observations from existing data. DM methods can also be viewed as statistical computation, artificial intelligence (AI) and database approach. Data mining techniques and algorithms encompass a variety of datasets like medical, geographical, web logs, agricultural data and many more. For each category of data or information, one has to apply the best suited algorithm to obtain the optimal results with highest accuracy. This is still a problem for many data mining tools as no unified theory has been adopted. The scientific community is very much conscious about this problematical issue and faced multiple challenges in establishing consensus over a unified data mining theory. The researchers have attempted to model the best fit algorithm for specific domain areas. Data mining has achieved tremendous success and many problems have been solved by using data mining techniques. But still there are some challenges in the field of data mining research which should be addressed. There are many data mining algorithms and techniques which are designed for individual problems, such as classification or clustering. A theoretical framework is required that unifies different data mining tasks including clustering, classification, interpretation and association rules which would help the field of data mining and provide a basis for future research. Advances in Data Mining Knowledge Discovery and Applications is intended to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The main contribution of this book is highlighting cutting edge fields and implementations of the knowledge discovery and data mining. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. The extensive use of computers and information technology has led toward the creation of extensive data repositories from a very wide variety of application areas. Such vast data repositories can contribute significantly towards future decision making provided appropriate knowledge discovery mechanisms are applied for extracting hidden, but potentially useful information embedded into the data. Data mining (DM) is one of the phases in knowledge discovery in databases. It is the process of extracting the useful information and knowledge in which the data is abundant, incomplete, ambiguous and random. DM is defined as an automated or semi-automated exploratory data analysis of large complex data sets that can be used to uncover patterns and relationships in data with an emphasis on large observational databases. Modern statistical and computational technologies are applied to the problem in order to find useful patterns hidden within a large database. To uncover hidden trends and patterns, DM uses a combination of an explicit knowledge base, sophisticated analytical skills, and domain knowledge. In effect, the predictive models formed from the trends and patterns through DM enable analysts to produce new observations from existing data. DM methods can also be viewed as statistical computation, artificial intelligence (AI) and database approach. Data mining techniques and algorithms encompass a variety of datasets like medical, geographical, web logs, agricultural data and many more. For each category of data or information, one has to apply the best suited algorithm to obtain the optimal results with highest accuracy. This is still a problem for many data mining tools as no unified theory has been adopted. The scientific community is very much conscious about this problematical issue and faced multiple challenges in establishing consensus over a unified data mining theory. The researchers have attempted to model the best fit algorithm for specific domain areas. Data mining has achieved tremendous success and many problems have been solved by using data mining techniques. But still there are some challenges in the field of data mining research which should be addressed. There are many data mining algorithms and techniques which are designed for individual problems, such as classification or clustering. A theoretical framework is required that unifies different data mining tasks including clustering, classification, interpretation and association rules which would help the field of data mining and provide a basis for future research. Advances in Data Mining Knowledge Discovery and Applications is intended to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The main contribution of this book is highlighting cutting edge fields and implementations of the knowledge discovery and data mining. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. The extensive use of computers and information technology has led toward the creation of extensive data repositories from a very wide variety of application areas. Such vast data repositories can contribute significantly towards future decision making provided appropriate knowledge discovery mechanisms are applied for extracting hidden, but potentially useful information embedded into the data. Data mining (DM) is one of the phases in knowledge discovery in databases. It is the process of extracting the useful information and knowledge in which the data is abundant, incomplete, ambiguous and random. DM is defined as an automated or semi-automated exploratory data analysis of large complex data sets that can be used to uncover patterns and relationships in data with an emphasis on large observational databases. Modern statistical and computational technologies are applied to the problem in order to find useful patterns hidden within a large database. To uncover hidden trends and patterns, DM uses a combination of an explicit knowledge base, sophisticated analytical skills, and domain knowledge. In effect, the predictive models formed from the trends and patterns through DM enable analysts to produce new observations from existing data. DM methods can also be viewed as statistical computation, artificial intelligence (AI) and database approach. Data mining techniques and algorithms encompass a variety of datasets like medical, geographical, web logs, agricultural data and many more. For each category of data or information, one has to apply the best suited algorithm to obtain the optimal results with highest accuracy. This is still a problem for many data mining tools as no unified theory has been adopted. The scientific community is very much conscious about this problematical issue and faced multiple challenges in establishing consensus over a unified data mining theory. The researchers have attempted to model the best fit algorithm for specific domain areas. Data mining has achieved tremendous success and many problems have been solved by using data mining techniques. But still there are some challenges in the field of data mining research which should be addressed. There are many data mining algorithms and techniques which are designed for individual problems, such as classification or clustering. A theoretical framework is required that unifies different data mining tasks including clustering, classification, interpretation and association rules which would help the field of data mining and provide a basis for future research. Advances in Data Mining Knowledge Discovery and Applications is intended to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The main contribution of this book is highlighting cutting edge fields and implementations of the knowledge discovery and data mining. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas.