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- W2770602261 abstract "A novel parallel implementation of the Evolving Clustering Method (ECM) is proposed in this paper. The original serial version of the ECM is the clustering method which computes online and with a single-pass. The parallel version (Parallel ECM or PECM) is implemented in the Apache Spark framework, which makes it work in real time. The parallelization of the algorithm aims to handle a dataset with large volume. Many of the extant clustering algorithms do not involve a parallel one-pass method. The proposed method addresses this shortcoming. Its effectiveness is demonstrated on a credit card fraud dataset (with size 297 MB), and a Higgs dataset was taken from Physics pertaining to particle detectors in the accelerator (with size 1.4 GB). The experimental setup included a cluster of 10 machines having 32 GB RAM each with Hadoop Distributed File System (HDFS) and Spark computational environment. A remarkable achievement of this research is a dramatic reduction in computational time compared to the serial version of the ECM. In future, the PECM shall be hybridized with other machine learning algorithms for solving large-scale regression and classification problems." @default.
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- W2770602261 date "2017-01-01" @default.
- W2770602261 modified "2023-09-25" @default.
- W2770602261 title "Parallel Evolving Clustering Method for Big Data Analytics Using Apache Spark: Applications to Banking and Physics" @default.
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- W2770602261 doi "https://doi.org/10.1007/978-3-319-72413-3_19" @default.
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