Mining Data Streams and Frequent Itemset

Frequent itemset mining is a branch of data mining that deals with the sequences of action. This chapter focuses on various itemset mining algorithms, namely nearest neighbor, similarity measure: the distance metric, artificial neural networks, support vector machines, linear regression, logistic regression, time‐series forecasting, big data and stream analytics, data stream mining. Several algorithms have been proposed to solve the frequent itemset problem. Some of the important itemset mining algorithms are: apriori algorithm, Eclat algorithm, and FP growth algorithm. The GenMax Algorithm is a highly efficient algorithm to determine the exact maximal frequent itemsets. Charm is an efficient algorithm for mining the set of all closed frequent itemsets. Data mining is the method of discovering the underlying pattern in large data sets to establish relationships and to predict outcomes though data analysis. Mining data streams is the process of extracting the underlying knowledge from the data streams that are arriving at high speed.