MuDi-Stream: A multi density clustering algorithm for evolving data stream

Amini, Amineh; Saboohi, Hadi; Herawan, Tutut; Wah, Teh Ying

doi:10.1016/j.jnca.2014.11.007

Cited by 73 publications

(82 citation statements)

References 30 publications

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“…where dist(p ij ) denotes the Euclidean distance between point p ij and the center c. The weight of a core-micro-cluster must be above or equal to a threshold , where is a user-defined parameter and the radius must be below or equal to a user defined boundary . The core-micro-cluster summary has been employed by DenStream [17], rDenStream [37], C-DenStream [46], HDDStream [40], MuDi-Stream [7], HDenStream [36], and PreDeConStream [28].…”

Section: Data Summariesmentioning

confidence: 99%

“…The number of clusters is not required as input, the input parameters concern the definition of the object's neighborhood and its density. Related data stream clustering algorithms are DenStream [17], rDenStream [37], C-DenStream [46], SDStream [43], HDDStream [40], MuDi-Stream [7], and HDenStream [36]. 3.…”

Section: Density-based Algorithms Consider Clusters Asmentioning

confidence: 99%

“…Similarly, PreDeConStream [28] handles high-dimensional data. Amini et al [7] proposed another high-dimensional stream clustering algorithm named MuDi-Stream. The algorithm has 2 phases, an online which consists of 3 components for keeping and maintaining the incoming objects in core mini-clusters and an offline for generating the final clusters.…”

Section: Density-based Stream Clusteringmentioning

confidence: 99%

“…MuDi-Stream [7] is an online-offline framework with 4 main components that combines CMCs with a hybrid method which uses a density-based method to generate the final clusters and a grid-based method to handle outliers. In the online phase, the algorithm keeps summary information about the evolving multidensity data stream in the form of CMCs and based on these CMCs in the offline phase the algorithm generates the final clusters, also the algorithm maintains a grid list in the form of tree with the grids that are under consideration for clustering.…”

Section: Complexity Analysismentioning

confidence: 99%

See 3 more Smart Citations

An evaluation of data stream clustering algorithms

Mansalis

Ntoutsi

Pelekis

et al. 2018

Statistical Analysis

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Data stream clustering is a hot research area due to the abundance of data streams collected nowadays and the need for understanding and acting upon such sort of data. Unsupervised learning (clustering) comprises one of the most popular data mining tasks for gaining insights into the data. Clustering is a challenging task, while clustering over data streams involves additional challenges such as the single pass constraint over the raw data and the need for fast response. Moreover, dealing with an infinite and fast changing data stream implies that the clustering model extracted upon such sort of data is also subject to evolution over time. Several stream clustering surveys exist already in the literature; however, they focus on a theoretical presentation of the surveyed algorithms. On the contrary, in this paper, we survey the state‐of‐the‐art stream clustering algorithms and we evaluate their performance in different data sets and for different parameter settings.

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Section: Data Summariesmentioning

confidence: 99%

Section: Density-based Algorithms Consider Clusters Asmentioning

confidence: 99%

Section: Density-based Stream Clusteringmentioning

confidence: 99%

Section: Complexity Analysismentioning

confidence: 99%

See 2 more Smart Citations

An evaluation of data stream clustering algorithms

Mansalis

Ntoutsi

Pelekis

et al. 2018

Statistical Analysis

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show abstract

“…But to do so, it is necessary to add a classification step at the end of the online MPPCA algorithm to provide the expected clustering. MuDi-Stream [20] is a hybrid grid-based multi-density clustering algorithm with online-offline phases. In the online phase, it keeps summary information of evolving multi-density data stream in the form of core micro-clusters.…”

Section: Related Workmentioning

confidence: 99%

A Novel High Dimensional and High Speed Data Streams Algorithm: HSDStream

Ahmed¹,

Ahmed²,

Shahzad³

2016

ijacsa

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Abstract-This paper presents a novel high speed clustering scheme for high-dimensional data stream. Data stream clustering has gained importance in different applications, for example, network monitoring, intrusion detection, and real-time sensing. High dimensional stream data is inherently more complex when used for clustering because the evolving nature of the stream data and high dimensionality make it non-trivial. In order to tackle this problem, projected subspace within the high dimensions and limited window sized data per unit of time are used for clustering purpose. We propose a High Speed and Dimensions data stream clustering scheme (HSDStream) which employs exponential moving averages to reduce the size of the memory and speed up the processing of projected subspace data stream. It works in three steps: i) initialization, ii) real-time maintenance of core and outlier micro-clusters, and iii) on-demand offline generation of the final clusters. The proposed algorithm is tested against high dimensional density-based projected clustering (HDDStream) for cluster purity, memory usage, and the cluster sensitivity. Experimental results are obtained for corrected KDD intrusion detection dataset. These results show that HSDStream outperforms the HDDStream in all performance metrics, especially, the memory usage and the processing speed.

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SubtStream: Online subtractive stream clustering algorithm

MİLLİ

Bulut

2022

Concurrency and Computation

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Real-time stream data processing has gained high importance with the rapid rise of big data trends in different areas such as social media, finance, business, science, and bioinformatics. Stream data can be characterized as fast, unstable, and big data sets.Due to these properties of stream data, it cannot be processed effectively with traditional algorithms. Just like stream data processing, clustering is also a difficult task. However, researchers have attempted to classify stream data by modifying traditional algorithms or designing new ones. So, in previous studies, incremental methods were used for clustering the stream data. This paper highlights the need to develop an efficient real-time clustering algorithm for data streams in the presence of concept high drift and an adaptive algorithm for different dimensions. The proposed clustering algorithm, SubtStream, combines decremental (subtractive property) and incremental (additivity property) strategies to overcome the high drift. It also introduces a new dimension-based approach to adopt the dimension change. We use three radius parameters, Predefined User Parameter, Proactive Adaptive Parameter, and Reactive Adaptive Parameter, to achieve adaptability. The proposed method, SubtStream, showed better performance on synthetic and real data sets.

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MuDi-Stream: A multi density clustering algorithm for evolving data stream

Cited by 73 publications

References 30 publications

An evaluation of data stream clustering algorithms

An evaluation of data stream clustering algorithms

A Novel High Dimensional and High Speed Data Streams Algorithm: HSDStream

SubtStream: Online subtractive stream clustering algorithm

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