As the number of sensor network application scenarios continues to grow, the security
problems inherent in this approach have become obstacles that hinder its wide application …

Under the co-promotion of the wave of urbanization and the rise of data science, smart cities
have become the new concept and new practice of urban development. Smart cities are the …

This paper explores five pattern mining problems and proposes a new distributed framework
called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM …

Data mining is widely applied to establish connections among the items in massive datasets
nowadays. Association rule mining is one of the most popular methods to perform data …

It is uncertain whether products will be manufactured as defective products. Producing the
defective products causes the waste of time and finance in the industrial fields. When the …

Since outliers are the major factors that affect accuracy in data science, many outlier
detection approaches have been proposed for effectively identifying the implicit outliers from …

Uncertain data mining has attracted so much interest in many emerging applications over
the past decade. An issue of particular interest is to discover the frequent itemsets in …

Itemset mining is a popular extension to the frequent pattern mining problem in data mining.
Finding infrequent patterns, however, has gained its importance due to proven utility in the …

Classification has been proposed for the automated detection of similarity groups in spatio-
temporal data. However, recent approaches have introduced clustering based solutions to …

With the advent of the era of big data, the Provenance Method of electronic archives based
on knowledge graph under the environment of big data has produced a large number of …