We analyze the drawbacks of DBSCAN and its variants, and find the grid technique, which is
used in Fast-DBSCAN and ρ-approximate DBSCAN, is almost useless in high dimensional …

Soft clustering, a widely utilized method in data analysis, offers a versatile and flexible
strategy for grou** data points. Most soft clustering algorithms assume that all the features …

Video content data have a variety of objects that could be associated with each other.
Although content data contains similar objects or themes, their associations can become …

Recent years have seen an increase in research on time series data mining (especially time-
series clustering) owing to the widespread existence of time series in various fields …

A novel procedure to perform fuzzy clustering of multivariate time series generated from
different dependence models is proposed. Different amounts of dissimilarity between the …

Time series classification is an important topic in data mining. Time series classification
methods have been studied by many researchers. A feature-weighted classification method …

Time series clustering has been used in diverse scientific areas to extract valuable
information from complex and massive time series datasets. To improve the quality and …

When data mining research is conducted, it is difficult to obtain precise domain knowledge to
set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing …

To successfully market to automotive parts customers in the Industrial Internet era, parts
agents need to perform effective customer analysis and management. Dynamic customer …

Knowledge-based clustering algorithms can improve traditional clustering models by
introducing domain knowledge to identify the underlying data structure. While there have …