Improving the Performance of KNN Classification Algorithms by Using Apache Spark (Record no. 9732)

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control field 20191016145145.0
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fixed length control field 191016b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 9898
Author Rajesh B.
245 ## - TITLE STATEMENT
Title Improving the Performance of KNN Classification Algorithms by Using Apache Spark
250 ## - EDITION STATEMENT
Volume, Issue number Vol.4(2), July-Dec
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Tamil Nadu
Name of publisher, distributor, etc. i-manager's
Year 2017
300 ## - PHYSICAL DESCRIPTION
Pagination 23-32p.
520 ## - SUMMARY, ETC.
Summary, etc. Data mining and machine learning are the most interesting research areas which find meaningful information from the large amount of data available, and converts into understandable form for further use. Diabetes is one of the growing diseases all over the world. Health trade professionals desire a reliable prediction system to diagnose polygenic disease. Tools and techniques available will be used to find the appropriate approaches and methods for classification of diabetes and in extracting valuable pattern. The Spark software was employed as a mining tool for diagnosing diabetes. Thus, using the spark, the performance of KNN Classification can be improved.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4622
Topical term or geographic name entry element Computer Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 9899
Co-Author Srinivasulu, Asadi
773 0# - HOST ITEM ENTRY
Title i-manager's journal on cloud computing (JCC)
Place, publisher, and date of publication Nagercoil i-manager Publication
International Standard Serial Number 2349-6835
856 ## - ELECTRONIC LOCATION AND ACCESS
URL https://www.imanagerpublications.com/article/14382/23
Link text Click here
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Source of classification or shelving scheme
Koha item type Articles Abstract Database
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          School of Engineering & Technology School of Engineering & Technology Archieval Section 2019-10-16 2019893 2019-10-16 2019-10-16 Articles Abstract Database
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