Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques, Fourth Edition, is a comprehensive guide to mining patterns, knowledge, and models from data for diverse applications. It covers preprocessing, characterization, warehousing, frequent patterns, associations, correlations, data classification, model construction, cluster analysis, outlier detection, and deep learning. It also discusses trends, applications, and research frontiers in data mining.

Format: Paperback / softback
Length: 752 pages
Publication date: 26 October 2022
Publisher: Elsevier Science & Technology

Data mining is a process of extracting patterns, knowledge, and models from various types of data for diverse applications. It involves the exploration and analysis of large collections of data, known as knowledge discovery from data (KDD), to uncover valuable insights and make informed decisions. In this comprehensive fourth edition, authors Michael Steinbach and Joris Piatetsky provide a thorough introduction to data mining concepts, principles, and techniques. They cover the essential steps involved in data mining, including preprocessing, characterization, and warehousing of data. The book then divides data mining methods into several major tasks, such as frequent pattern mining, association rule mining, correlation analysis, data classification, model construction, cluster analysis, and outlier detection. Additionally, a dedicated chapter is devoted to introducing deep learning concepts and methods, which have gained significant popularity in recent years. Finally, the book explores the latest trends, applications, and research frontiers in data mining, providing readers with a comprehensive understanding of this rapidly evolving field.

The authors begin by providing an overview of data mining, explaining its significance in today's data-driven world. They discuss the challenges faced by organizations in managing and analyzing large amounts of data and the importance of data mining in extracting valuable information and making informed decisions. The book then delves into the preprocessing stage, where raw data is cleaned, transformed, and standardized to ensure its quality and suitability for further analysis. Preprocessing techniques include data cleaning, data integration, feature extraction, and dimensionality reduction. The authors explain each technique in detail, providing examples and practical applications to illustrate their effectiveness. They also discuss the importance of data characterization, which involves identifying the characteristics and properties of the data that can be used for mining. This step helps in selecting the appropriate data mining techniques and algorithms. After preprocessing, the book moves on to the characterization stage, where the authors introduce concepts and methods for mining frequent patterns, associations, and correlations in large data sets. They discuss the use of association rule. Rule mining algorithms, such as Apriori, Eclat, and FP-Growth, to identify patterns that occur frequently in the data. The authors also introduce the concept of correlation analysis, which involves identifying relationships between variables and their associations. They discuss the use of correlation matrices, such as Pearson's correlation coefficient, Spearman's rank correlation coefficient, and Kendall's tau correlation, to measure the strength and direction. The book then discusses the use of data classification and model construction for data mining. The authors introduce the concept of supervised learning, which involves training a model on labeled data to predict the labels of new data. They discuss the use of unsupervised learning, which involves training a model on unlabeled data to identify patterns and relationships between variables. They also discuss the use of decision trees. Tree algorithms, such as decision trees, random forests, and gradient descent, to classify data and make predictions. The authors also discuss the use of clustering. Neural networks, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, to analyze and process complex data. The book then covers cluster analysis, which involves grouping. The use of clustering algorithms, such as hierarchical clustering, k-means clustering, and DBSCAN clustering, to group data into meaningful clusters. The authors discuss the use of outlier detection, which involves identifying the identification. The use of outlier detection algorithms, such as the Z-score, Mahalanobis distance, and anomaly detection algorithms, to identify outliers in data sets. The authors also discuss the use of deep learning, which involves training a model on labeled data to predict the labels of new data. They discuss the use of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, to analyze and process complex data. The book then covers the trends. The use of deep learning, which involves training a model on labeled data to predict the labels of new data. They discuss the use of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, to analyze and process complex data. The book then covers the trends, applications, and research frontiers in data mining, providing readers with a comprehensive understanding of this rapidly evolving field.

Weight: 1176g
Dimension: 193 x 233 x 36 (mm)
ISBN-13: 9780128117606
Edition number: 4 ed