Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java

Summary This work attempts to analyze the limits of Weka Data Miner in executing the Simple K‐Means algorithm and makes an attempt to identify how much data is too much data for the Weka Data Miner to execute the algorithm. This work is further based on developing a distributed processing model to o...

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Bibliographic Details
Published inConcurrency and computation Vol. 31; no. 14
Main Authors Sudarsan, V., Sugumar, R.
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc 25.07.2019
Subjects
Algorithms
Distance measurement
Distributed processing
distributed processing in K‐Means
distributed processing in Weka
distributed processing of Euclidean distance
Euclidean geometry
RMI call back server in Java
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Summary:Summary This work attempts to analyze the limits of Weka Data Miner in executing the Simple K‐Means algorithm and makes an attempt to identify how much data is too much data for the Weka Data Miner to execute the algorithm. This work is further based on developing a distributed processing model to offer a better solution in handling large datasets. The required features are implemented using the RMI Call back Server. The Euclidean Distance measure is considered for calculating the distance.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5313