DATA MINING BASED STREAM MINING APPROACH : A STREAM MINING BASED APPROACH FOR DYNAMIC ENVIRONMENT USING K-MEANS++ ALGORITHM

Language: English

Published by LAP LAMBERT Academic Publishing, 2024

6207466624 / 9786207466627

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nach der Bestellung gedruckt Neuware - Printed after ordering - The Clustering is one of the most important technique in data mining. It aims partitioning the data into groups of similar objects. That is refered to as clusters. This research compares the StreamKM++ algorithm with the existing work, such as AP, IAPKM and IAPNA. The StreamKM++ algorithm is a new clustering algorithm from the data stream and itto constructs a good clustering of the stream, using a small amount of memory and time.Many researchers have done their work with static clustering algorithm, but in real time the data is dynamic in nature. Such as blogs, web pages, audio and video, etc., Hence, the conventional static technique doesn't support in real time environment. In this work, the StreamKM++ algorithm is used which achieves high clustering performance over traditional AP, IAPKM and IAPNA. The experimental result shows StreamKM++ algorithm achieves the best result compared with existing work. It has increased the average accuracy rate and reduced the computational time, memory and number of iterations.…

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Title
DATA MINING BASED STREAM MINING APPROACH : A STREAM MINING BASED APPROACH FOR DYNAMIC ENVIRONMENT USING K-MEANS++ ALGORITHM
Author
Shylaja S
Publisher
LAP LAMBERT Academic Publishing
Publication year
2024
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6207466624
ISBN 13
9786207466627
Item weight
149 grams
Dimensions
220x150x6 mm

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