Data Mining
Innovative and Efficient Techniques
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Data mining is the process of automatically extracting new and useful knowledge hidden in large datasets. This book focuses on the enhancement of following three data mining techniques for achieving the better mining results: Association Rule Mining (ARM), Clustering Classification In Association Rule Mining (ARM), two algorithms known as Apriori algorithm and FP-Growth algorithm have been enhanced for better mining results. An efficient partitional clustering algorithm utilizing the well-known technique, k-means clustering is proposed in this book to tackle the problem of empty clusters. Clas...
Data mining is the process of automatically extracting new and useful knowledge hidden in large datasets. This book focuses on the enhancement of following three data mining techniques for achieving the better mining results: Association Rule Mining (ARM), Clustering Classification In Association Rule Mining (ARM), two algorithms known as Apriori algorithm and FP-Growth algorithm have been enhanced for better mining results. An efficient partitional clustering algorithm utilizing the well-known technique, k-means clustering is proposed in this book to tackle the problem of empty clusters. Classification operation usually uses supervised learning methods that induce a classification model from a database. The k-Nearest Neighbor (k-NN) is one of the simplest classification methods used in data mining and machine learning. in this book, the proposed algorithm improved the performance of conventional k-NN algorithm by identifying the optimal value of k.
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