论文引用

Heri Ramampiaro, Florent Masseglia, Kjetil Nørvåg et al.,
2020,
Eng. Appl. Artif. Intell.

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...

Hongqing Zhu, Xu Pan, Qunyi Xie,
2017,
Neurocomputing

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...

Witold Pedrycz, Adam Kiersztyn, Paweł Karczmarek et al.,
2020,
Knowl. Based Syst.

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...

Witold Pedrycz, Orion Fausto Reyes-Galaviz, W. Pedrycz et al.,
2017,
Fuzzy Sets Syst.

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...

Wei Zhang, Hongwei Liu, Lan Du et al.,
2018,
Pattern Recognit.

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...

Witold Pedrycz, ZhiWu Li, Mingming Liu et al.,
2017,
Knowl. Based Syst.

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...

Witold Pedrycz, Hesam Izakian, Iqbal Jamal et al.,
2015,
Eng. Appl. Artif. Intell.

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...

Jiuwen Cao, Houpan Zhou, Hongyun Qin et al.,
2020,
Neurocomputing

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...

Zhou Fang, Houpan Zhou, Hongyun Qin et al.,
2019 9th International Conference on Information Science and Technology (ICIST)

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...

Aihua Zhou, Jie Ding, Lipeng Zhu et al.,
2015 IEEE International Conference on Progress in Informatics and Computing (PIC)

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...