Incremental density-based ensemble clustering over evolving data streams

Khan, Imran; Huang, Joshua Zhexue; Ivanov, Kamen

doi:10.1016/j.neucom.2016.01.009

Cited by 52 publications

(18 citation statements)

References 25 publications

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“…Khan et al [8] suggested an algorithm allowing the discovery of clusters in the data of smart meters in real-time. The algorithm consists of three phases.…”

Section: Of 25mentioning

confidence: 99%

“…Such segmentation can be used in an integrated planning system where an adequate choice in real-time among the available load management alternatives is critical to effectively meet the requirements of the system [9]. Electricity consumer consumption patterns also provide valuable information to determine optimal tariffs [8,10]. Load profiling is becoming one of the most appropriate methods for dealing effectively with customer energy consumption data.…”

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confidence: 99%

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Simulation Study on the Electricity Data Streams Time Series Clustering

et al. 2020

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Currently, thanks to the rapid development of wireless sensor networks and network traffic monitoring, the data stream is gradually becoming one of the most popular data generating processes. The data stream is different from traditional static data. Cluster analysis is an important technology for data mining, which is why many researchers pay attention to grouping streaming data. In the literature, there are many data stream clustering techniques, unfortunately, very few of them try to solve the problem of clustering data streams coming from multiple sources. In this article, we present an algorithm with a tree structure for grouping data streams (in the form of a time series) that have similar properties and behaviors. We have evaluated our algorithm over real multivariate data streams generated by smart meter sensors—the Irish Commission for Energy Regulation data set. There were several measures used to analyze the various characteristics of a tree-like clustering structure (computer science perspective) and also measures that are important from a business standpoint. The proposed method was able to cluster the flows of data and has identified the customers with similar behavior during the analyzed period.

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“…Khan et al [8] suggested an algorithm allowing the discovery of clusters in the data of smart meters in real-time. The algorithm consists of three phases.…”

Section: Of 25mentioning

confidence: 99%

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confidence: 99%

Simulation Study on the Electricity Data Streams Time Series Clustering

et al. 2020

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“…The Genetic Algorithm considered in this paper can be included in the area of time series segmentation [7,24,25,26,9,10,11,17,13]. Our main objective is to devise an unsupervised methodology to identify time segments with similar statistical behaviour [23].…”

Section: Summary Of the Algorithmmentioning

confidence: 99%

“…Time series segmentation is a research field, aiming to provide a compact representation of the time series values, dividing it into segments and using an abstract representation of each segment. It is very important for time series representation and time series mining [6,7,8] and is commonly used as a pre-processing step for different mining tasks [8,9,10,11] (e.g. clustering, classification or motif detection) and for data compressing [12,13].…”

Section: Introductionmentioning

confidence: 99%

On the use of evolutionary time series analysis for segmenting paleoclimate data

et al. 2019

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Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series.

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“…No entanto, realizar a leitura, análise e a extração de conhecimento do grande fluxo de dados de uma rede de sensores é uma tarefa custosa e que demanda métodos computacionais sofisticados (GAMA, 2010), por se tratar de uma grande quantidade de sensores, produzindo dados de maneira contínua em um ambiente dinâmico (GAMA, 2012). Logo, desafios científicos inerentes a esses ambientes têm motivado diversos trabalhos de pesquisa envolvendo redes de sensores e mineração de fluxos de dados (data stream mining)(HYDE; ANGELOV; MACKENZIE, 2017; SILVA; HRUSCHKA; GAMA, 2017;DING et al, 2016;KHAN;IVANOV, 2016;PEREIRA;MELLO, 2014;?? ;FORESTIERO;PIZZUTI;SPEZZANO, 2013).…”

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Agrupamento de fluxos de dados utilizando dimensão fractal

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Incremental density-based ensemble clustering over evolving data streams

Cited by 52 publications

References 25 publications

Simulation Study on the Electricity Data Streams Time Series Clustering

Simulation Study on the Electricity Data Streams Time Series Clustering

On the use of evolutionary time series analysis for segmenting paleoclimate data

Agrupamento de fluxos de dados utilizando dimensão fractal

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