swMATH ID: 23392
Software Authors: Michael Hahsler, Matthew Bolaños, John Forrest
Description: Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R. In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary data generating process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern mining. New algorithms for these types of data are proposed regularly and it is important to evaluate them thoroughly under standardized conditions. In this paper we introduce stream, a research tool that includes modeling and simulating data streams as well as an extensible framework for implementing, interfacing and experimenting with algorithms for various data stream mining tasks. The main advantage of stream is that it seamlessly integrates with the large existing infrastructure provided by R. In addition to data handling, plotting and easy scripting capabilities, R also provides many existing algorithms and enables users to interface code written in many programming languages popular among data mining researchers (e.g., C/C++, Java and Python). In this paper we describe the architecture of stream and focus on its use for data stream clustering research. stream was implemented with extensibility in mind and will be extended in the future to cover additional data stream mining tasks like classification and frequent pattern mining.
Homepage: https://cran.r-project.org/web/packages/stream/index.html
Source Code:  https://github.com/cran/stream
Dependencies: R
Keywords: data streams; data mining; clustering; Journal of Statistical Software; www.jstatsoft.org; Data Stream Clustering Research
Related Software: UCI-ml; R; RStorm; streamMOA; Rcpp; animation; RMOA; rJava; Rlecuyer; quantmod; DBI; HadoopStreaming; clusterGeneration; proxy; cluster (R); rstream; mlbench; fpc; rEMM; dbscan
Cited in: 2 Documents

Cited in 1 Serial

2 Machine Learning

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