This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …

With the emergence of machine learning methods, data-driven fault diagnosis has gained
significant attention in recent years. However, traditional data-driven diagnosis approaches …

To understand current situation in specific scenarios, valuable knowledge should be mined
from both historical data and emerging new data. However, most existing algorithms take the …

Spatial data science emerges as an important subclass of data science and focuses on
extracting meaningful information and knowledge from spatial data to enable effective …

ABSTRACT Market Basket Analysis is an observational data mining methodology to
investigate the consumer buying behavior patterns in retail Supermarket. It analyzes …

This article presents a new differential evolution (DE) algorithm for mining optimized
statistically significant fuzzy association rules that are abundant in number and high in rule …

Most pattern mining approaches such as association rule mining or frequent itemsets mining
can only handle, however, the binary database in which each item or attribute is represented …

Classification based on association rules gets more and more interest in research and
practice. In many contexts, rules are often mined from sparse data in high-dimensional …

Land use/cover changes (LULC) are complicated and regionally diverse. When mining
regional patterns, the use of a spatial relationship that is determined without considering the …

Market basket analysis is a trendy topic in the field of data mining. Analyzing the daily
transactions, several types of associations were noticed in the transacted item sets. The …