The age of big data is now coming. But the traditional data analytics may not be able to
handle such large quantities of data. The question that arises now is, how to develop a high …

Pattern mining is a fundamental technique of data mining to discover interesting correlations
in the data set. There are several variations of pattern mining, such as frequent itemset …

With the growing popularity of shared resources, large volumes of complex data of different
types are collected automatically. Traditional data mining algorithms generally have …

Many parallelization techniques have been proposed to enhance the performance of the
Apriori-like frequent itemset mining algorithms. Characterized by both map and reduce …

DBSCAN is a well-known algorithm for density-based clustering because it can identify the
groups of arbitrary shapes and deal with noisy datasets. However, with the increasing …

Abstract High Utility Sequential Pattern mining (HUSP) algorithms aim to find all the high
utility sequences from a sequence database. Due to the large explosion of data, recently few …

Clustering techniques are very attractive for identifying and extracting patterns of interests
from datasets. However, their application to very large spatial datasets presents numerous …

The problem of mining high utility sequential patterns (HUSP) has been studied recently.
Existing solutions are mostly memory-based, which assume that data can fit into the main …

Abstract Sequential Pattern Mining (SPM) is a popular data mining task with broad
applications. With the advent of big data, traditional SPM algorithms are not scalable. Hence …

As the information technology spreads fast, most of the data were born digital as well as
exchanged on internet today. According to the estimation of Lyman and Varian [1], the new …