Introduction to <b>stream</b>: An Extensible Framework for Data Stream Clustering Research with <i>R</i>

Hahsler, Michael; Bolanos, Matthew; Forrest, John

doi:10.18637/jss.v076.i14

Cited by 43 publications

(30 citation statements)

References 47 publications

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“…In this article, we will discuss methods of data stream clustering. There are various implementations dedicated for this purpose, such as the stream package in R [11], Massive Online Analysis software written in Java [12] or scikit-learn library in Python [13]. Unfortunately, this software provides methods for only one stream and not multiple stream processing.…”

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confidence: 99%

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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confidence: 99%

Simulation Study on the Electricity Data Streams Time Series Clustering

et al. 2020

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“…It is a java‐based software package that contains state‐of‐the‐art algorithms and evaluations measures for running experiments. Recently, an R package called Steam published that allows to perform clustering experiments, the main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R.…”

Section: Methodsmentioning

confidence: 99%

“…It is a java-based software package that contains state-of-the-art algorithms and evaluations measures for running experiments. Recently, an R package called Steam [27] published that allows to perform clustering experiments, the main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R. Also, in order to clear any ambiguity between the terms window size w and horizon H, window size w defines the number of data objects which arrive in each time period and horizon means the number of windows in which the clustering analysis is evaluated; we have set for the entire evaluation analysis that horizon. H = 1.…”

Section: More Experimental Settingsmentioning

confidence: 99%

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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“…The extended D-Stream algorithm increases the cluster feature but can also have the reverse effect. Introduced a research tool that includes modelling and simulating data streams [18] extensible framework used for implementing, interfacing and experimenting with algorithms for several data stream mining tasks. Dataset Used-KDD Cup'99 dataset used.…”

Section: Literature Reviewmentioning

confidence: 99%

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

Mulla,

Patil

2019

IJEAT

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Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R

Cited by 43 publications

References 47 publications

Simulation Study on the Electricity Data Streams Time Series Clustering

Simulation Study on the Electricity Data Streams Time Series Clustering

An evaluation of data stream clustering algorithms

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

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