Book Chapter10.1007/978-3-642-22577-2_46
Analysis and Study of Incremental K-Means Clustering Algorithm
Sanjay Chakraborty,Naresh Kumar Nagwani +1 more
- 19 Jul 2011
- pp 338-341
TL;DR: In incremental approach, the K-means clustering algorithm is applied to a dynamic database where the data may be frequently updated, and this approach measure the new cluster centers by directly computes the new data from the means of the existing clusters instead of rerunning the K.means algorithm.
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Abstract: Study of this paper describes the incremental behaviours of partitioning based K-means clustering. This incremental clustering is designed using the cluster’s metadata captured from the K-Means results. Experimental studies shows that this clustering outperformed when the number of clusters increased, number of objects increased, length of the cluster radius decreased, while the incremental clustering outperformed when the number of new data objects are inserted into the existing database. In incremental approach, the K-means clustering algorithm is applied to a dynamic database where the data may be frequently updated. And this approach measure the new cluster centers by directly computes the new data from the means of the existing clusters instead of rerunning the K-means algorithm. Thus it describes, at what percent of delta change in the original database up to which incremental K-means clustering behaves better than actual K-means. It can be also used for large multidimensional dataset.
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Big data analytics in bioinformatics: architectures, techniques, tools and issues
TL;DR: The issues and challenges posed by several big data problems in bioinformatics are addressed, and an overview of the state of the art and the future research opportunities are given.
49
Incremental kernel spectral clustering for online learning of non-stationary data
TL;DR: The IKSC model is developed to quickly adapt itself to a changing environment, in order to learn evolving clusters with high accuracy, and is able to precisely recognize the dynamics of shifting patterns in a non-stationary context.
44
•Posted Content
Performance Comparison of Incremental K-means and Incremental DBSCAN Algorithms
TL;DR: In this article, the performance evaluation of incremental DBSCAN clustering algorithm is implemented and most importantly it is compared with the performance of incremental K-means algorithm and it also explains the characteristics of these two algorithms based on the changes of the data in the database.
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ICFS Clustering With Multiple Representatives for Large Data
TL;DR: This paper discusses two challenges, i.e., assignment of new arriving objects and dynamic adjustment of clusters, in incremental CFS (ICFS) clustering, and proposes two ICFS clustering algorithms, ICFS with multiple representatives (ICfsMR) and the enhanced ICFSMR (E_ICFSMR) to tackle the two challenges.
42
A bibliometric survey on incremental clustering algorithm for electricity smart meter data analysis
Archana Chaudhari,Preeti Mulay +1 more
- 01 Dec 2019
TL;DR: The purpose of the paper is to dig out all the researches in smart meter data analytics and incremental clustering to make the concept clear for future researchers.
35
References
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Ian H. Witten,Eibe Frank,Mark Hall +2 more
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TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Data Mining: Concepts, Models, Methods, and Algorithms
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TL;DR: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making.
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•Proceedings Article
Incremental Clustering for Mining in a Data Warehousing Environment
Martin Ester,Hans-Peter Kriegel,Jörg Sander,Michael Wimmer,Xiaowei Xu +4 more
- 24 Aug 1998
TL;DR: It can be proven that the incremental algorithm yields the same result as DBSCAN, which is applicable to any database containing data from a metric space, e.g., to a spatial database or to a WWW-log database.
•Journal Article
An Incremental Grid Density-Based Clustering Algorithm
TL;DR: A grid density-based clustering algorithm——GDCA is introduced, which discovers clusters with arbitrary shape in spatial databases, and first partitions the data space into a number of units, and then deals with units instead of points.
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