Publisher's Synopsis
Knowledge Discovery in Databases (KDD) is an iterative process of extracting implicit, previously unknown, and potentially useful knowledge as a production factor from large datasets. It includes data selection, cleaning, integration, transformation, data mining (DM), and reporting. The KDD process consists of steps that are performed before conducting data mining (i.e., selection, pre-processing, and transformation of data), the actual DM, and subsequent steps (i.e., interpretation, and evaluation of results). DM refers to the specific step of applying one or more statistical, machine-learning, or image processing algorithms to a particular dataset with the intent to extract useful patterns from the datasets. DM is widely used in market segmentation, customer profiling, fraud detection, retail promotions, and credit risk analysis. Knowledge Discovery and Data Mining Applications is a compilation of original technical papers in both the research and practice of data mining and knowledge discovery, and detailed descriptions of significant applications. This volume presents knowledge discovery and data mining applications in two different sections. As known that data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization. It will be of comprehensive guide for data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques. Data mining helps finding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases. In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other benefits which bring a considerable competitive advantage to the organization. The main purpose of these techniques is to find patterns and hidden (but relevant) relations that might lead to revenue increase. Data mining techniques reside from classic statistical calculation, from database administration and from artificial intelligence. They are not a substitute for traditional statistical techniques, but an extension of graphical and statistical techniques. Data mining uses a large variety of statistical algorithms, shape recognition, classification, fuzzy logic, machine learning, genetic algorithms, neural networks, data viewing etc., from which we can mention regression algorithms, decision algorithms, neural networks, clustering analysis. Knowledge Discovery in Databases (KDD) is an iterative process of extracting implicit, previously unknown, and potentially useful knowledge as a production factor from large datasets. It includes data selection, cleaning, integration, transformation, data mining (DM), and reporting. The KDD process consists of steps that are performed before conducting data mining (i.e., selection, pre-processing, and transformation of data), the actual DM, and subsequent steps (i.e., interpretation, and evaluation of results). DM refers to the specific step of applying one or more statistical, machine-learning, or image processing algorithms to a particular dataset with the intent to extract useful patterns from the datasets. DM is widely used in market segmentation, customer profiling, fraud detection, retail promotions, and credit risk analysis. Knowledge Discovery and Data Mining Applications is a compilation of original technical papers in both the research and practice of data mining and knowledge discovery, and detailed descriptions of significant applications. This volume presents knowledge discovery and data mining applications in two different sections. As known that data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization. It will be of comprehensive guide for data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques. Data mining helps finding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases. In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other benefits which bring a considerable competitive advantage to the organization. The main purpose of these techniques is to find patterns and hidden (but relevant) relations that might lead to revenue increase. Data mining techniques reside from classic statistical calculation, from database administration and from artificial intelligence. They are not a substitute for traditional statistical techniques, but an extension of graphical and statistical techniques. Data mining uses a large variety of statistical algorithms, shape recognition, classification, fuzzy logic, machine learning, genetic algorithms, neural networks, data viewing etc., from which we can mention regression algorithms, decision algorithms, neural networks, clustering analysis. Knowledge Discovery in Databases (KDD) is an iterative process of extracting implicit, previously unknown, and potentially useful knowledge as a production factor from large datasets. It includes data selection, cleaning, integration, transformation, data mining (DM), and reporting. The KDD process consists of steps that are performed before conducting data mining (i.e., selection, pre-processing, and transformation of data), the actual DM, and subsequent steps (i.e., interpretation, and evaluation of results). DM refers to the specific step of applying one or more statistical, machine-learning, or image processing algorithms to a particular dataset with the intent to extract useful patterns from the datasets. DM is widely used in market segmentation, customer profiling, fraud detection, retail promotions, and credit risk analysis. Knowledge Discovery and Data Mining Applications is a compilation of original technical papers in both the research and practice of data mining and knowledge discovery, and detailed descriptions of significant applications. This volume presents knowledge discovery and data mining applications in two different sections. As known that data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing, and data visualization. It will be of comprehensive guide for data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques. Data mining helps finding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases. In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other benefits which bring a considerable competitive advantage to the organization. The main purpose of these techniques is to find patterns and hidden (but relevant) relations that might lead to revenue increase. Data mining techniques reside from classic statistical calculation, from database administration and from artificial intelligence. They are not a substitute for traditional statistical techniques, but an extension of graphical and statistical techniques. Data mining uses a large variety of statistical algorithms, shape recognition, classification, fuzzy logic, machine learning, genetic algorithms, neural networks, data viewing etc., from which we can mention regression algorithms, decision algorithms, neural networks, clustering analysis.
