Abstract This paper provides a short overview of space–time series clustering, which can be generally grouped into three main categories such as: hierarchical, partitioning-based, and overlapping clus...
Abstract This paper addresses an efficient scheme for clustering time-series through a novel regression mixture strategy (RMM) that simultaneously utilizes the benefits of the Markov random field (MRF...
Abstract The task of anomaly detection in data is one of the main challenges in data science because of the wide plethora of applications and despite a spectrum of available methods. Unfortunately, ma...
Abstract Owning to their abilities to reveal structural relationships in data, fuzzy clustering plays a pivotal role in fuzzy modeling, pattern recognition, and data analysis. As supporting an unsuper...
Abstract In the problem of one-class classification, one-class classifier (OCC) tries to identify objects of a specific class, called the target class, among all objects, by learning from a training s...
Abstract In the area of time series representation, the Piecewise Aggregate Approximation (PAA) method has established itself quite visibly resulting in a number of useful results. However, the PAA te...
Abstract Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the...
Abstract Abnormal electricity consumption (AEC) caused huge economic losses to power supply enterprises in the past years, and also posed severe threats to the safety of peoples’ daily live. An accura...
Abnormal electricity consumption (AEC) seriously affects the management of the power grid marketing department, causing huge economic losses to the power supply enterprises. An accurate abnormal elect...
Abnormal detection of electrical data has been widely used in the electric power industry. However, traditional abnormal detection algorithms mainly focus on the abnormal value in data of power consum...