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Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java
Author(s) -
Sudarsan V.,
Sugumar R.
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5313
Subject(s) - java , computer science , feature (linguistics) , data mining , measure (data warehouse) , simple (philosophy) , euclidean distance , operating system , artificial intelligence , philosophy , linguistics , epistemology
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.