For highly reliable gas turbines that rarely suffer faults, the overwhelming majority of
historical data are collected under healthy state, while only a very small number of them are …

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or
the data classes are unbalanced and close to each other, even in two dimensions. A novel …

Data mining is the process of extracting useful unknown knowledge from large datasets.
Frequent itemset mining is the fundamental task of data mining that aims at discovering …

This paper addresses the problem of responding to user queries by fetching the most
relevant object from a clustered set of objects. It addresses the common drawbacks of cluster …

The recent technology development fascinates the people towards information and its
services. Managing the personal and pubic data is a perennial research topic among …

This research presents a new generic deep learning (DL) framework for anomaly detection
in the Internet of Everything (IoE). It combines decomposition methods, deep neural …

This study uses the publicly available International Medical Devices Database in three-
phase research to present empirically grounded multi-dimensional typologies of events …

This paper considers discovering frequent itemsets in transactional databases and
addresses the time complexity problem by using high performance computing (HPC). Three …

This paper addresses the hashtag recommendation problem using high average-utility
pattern mining. We introduce a novel framework called PM-HRec (Pattern Mining for …

High average utility pattern mining has been proposed to overcome the demerits of high
utility pattern mining. Since high average utility pattern mining can extract more valuable …