Clustering Data Streams Based on Shared Density between Micro-Clusters

Hahsler, Michael; Bolaos, Matthew

doi:10.1109/tkde.2016.2522412

Cited by 126 publications

(96 citation statements)

References 23 publications

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“…(1) Density-based micro-clustering algorithms group, and (2) Density grid-based clustering algorithms group. Some of the existing density-based micro-clustering algorithms are: DenStream [5], OpticsStream [6], C-DenStream [7], rDenStream [8], SDStream [9], HDenStream [10], SOStream [11], HDDStream [12], PreDeConStream [13], FlockStream [14], LeaDen-Stream [15], DBStream [16]. All these algorithms use two stage online-offline framework.…”

Section: Related Workmentioning

confidence: 99%

“…To address the same problem, LeaDenStream [15] introduced a concept in which micro-clusters are represented by mini-micro leaders based on the distribution of data points in the micro-clusters, and it uses this representation for reclustering. DBStream [16] is the first density-based clustering method that clusters the micro-clusters based on shared density between the micro-clusters. DBStream captures the shared density information in the online component via a shared density graph, and it uses the same information in the offline phase for reclustering the micro-clusters.…”

Section: Related Workmentioning

confidence: 99%

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MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction

K¹,

Bindu²

2018

IJET

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Real-time data stream clustering has been widely used in many fields, and it can extract useful information from massive sets of data. Most of the existing density-based algorithms cluster the data streams based on the density within the micro-clusters. These algorithms completely omit the data density in the area between the micro-clusters and recluster the micro-clusters based on erroneous assumptions about the distribution of the data within and between the micro-clusters that lead to poor clustering results. This paper describes a novel density-based clustering algorithm for evolving data streams called MCDAStream, which clusters the data stream based on micro-cluster density and attraction between the micro-clusters. The attraction of micro-clusters characterizes the positional information of the data points in each micro-cluster. We generate better clustering results by considering both micro-cluster density and attraction of microclusters. The quality of the proposed algorithm is evaluated on various synthetic and real-time datasets with distinct characteristics and quality metrics.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction

K¹,

Bindu²

2018

IJET

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“…Data streams are the continuous flow of data and its size has no bounds [2][10]. Many applications produce this type of streaming data like GPS data from vehicles, web click stream data, computer network monitoring, readings from sensors etc.…”

Section: Imentioning

confidence: 99%

“…Clustering of data streams can be done by using grid based algorithms like D-Stream [1] or density based algorithms like DBSTREAM [2] or partitioning based algorithms like k-means. The main or primary goal of this paper is to improve the quality of final clusters and to reduce the time in generating the micro-clusters.…”

Section: Imentioning

confidence: 99%

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Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

Praneetha¹

2017

IJARCSSE

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Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.Index: Data Stream Clustering, Density based Clustering. I.I INTRODUCTION Clustering is a standard or imperative system of exploratory information mining, which isolates an arrangement of information into a few gatherings (additionally called clusters) such that items in same gathering are more comparable with each other in some sense than with the items in different gatherings. Data streams are the continuous flow of data and its size has no bounds [2][10]. Many applications produce this type of streaming data like GPS data from vehicles, web click stream data, computer network monitoring, readings from sensors etc. Data stream clustering is done for better understanding of data.Cluster algorithms and their parameter settings depend on the individual data sets. Data stream clustering algorithms process the data quickly by providing timely results, detects whether new clusters should appear or disappear and also identifies the outliers.Clustering of data streams can be done by using grid based algorithms like D-Stream [1] or density based algorithms like DBSTREAM [2] or partitioning based algorithms like k-means. The main or primary goal of this paper is to improve the quality of final clusters and to reduce the time in generating the micro-clusters. II.RELATED WORK In the application point of view one-pass clustering algorithms are not useful as the outdated data makes the cluster quality poor. CluStream is an effective and efficient method characterizes the data streams in different time horizons. The micro-clusters are stored as snapshots in pyramidal time window [5]. But cannot find arbitrary shaped clusters and cannot handle outliers. [6].Density based clustering algorithm, DBSCAN is used to find the clusters of arbitrary shapes in large spatial Databases with noise and it requires only one input parameter. It counts the number of data points and estimates its density by using eps, midpoints parameters and identifies the core, border and noise points. [3].The disadvant...

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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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Clustering Data Streams Based on Shared Density between Micro-Clusters

Cited by 126 publications

References 23 publications

MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction

MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

SubtStream: Online subtractive stream clustering algorithm

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