An Extended Linked Clustering Algorithms for Spatial Data Sets

An Extended Linked Clustering Algorithms for Spatial Data Sets

K Lakshmaiah

Contact

Research Scholar.

Contact

α Jawaharlal Nehru Technological University, Hyderabad

An Extended Linked Clustering Algorithms for Spatial Data Sets

Article Fingerprint

ReserarchID

CSTSDE6DVPK

An Extended Linked Clustering Algorithms for Spatial Data Sets Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

Spatial data mining techniques and for the most part conveyed clustering are broadly utilized as a part of the most recent decade since they manage huge and differing datasets which can’t be assembled midway. Current disseminated clustering approaches are typically producing universal models by amassing neighborhood outcomes that are acquired on every region. While this approach mines the data collections on their areas the accumulation stage is more perplexing, which may deliver inaccurate, and equivocal all universal clusters and in this manner mistaken learning. In this paper we propose an Extended Linked clustering approach for each huge spatial data collections that are assorted and appropriated. The approach in view of K-means algorithm yet it produces the quantity of all universal clusters progressively. In addition this approach utilizes an explained collection stage. The conglomerations stage is outlined in such way that the general procedure is proficient in time and memory assignment .Preliminary outcomes demonstrate that the proposed approach delivers excellent outcomes and scales up well. We likewise contrasted it with two prominent clustering algorithms and demonstrate that this approach is substantially more proficient.

References

34 Cites in Article

Reference Format

Usama Fayyad,Gregory Piatetsky-Shapiro,Padhraic Smyth (1996). The KDD process for extracting useful knowledge from volumes of data.
Alex Freitas,Simon Lavington (2000). Mining Very Large Databases with Parallel Processing.
I Foster,C Kesselman (1999). The Grid: Blueprint for a New Computing Infrastructure.
Thomas Dietterich (2000). An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization.
Hillol Kargupta,Philip Chan,Vipin Kumar,Zoran Obradovic (2000). Distributed and parallel knowledge discovery (workshop session) (title only).
R,J Shafer (1996). Parallel mining of association rules.
L-M Aouad,N-A Le-Khac,M-T Kechadi (2010). Performance study of a distributed apriori-like frequent itemsets mining technique.
Lamine Aouad,Nhien An-Lekhac,Tahar Kechadi (2009). Grid-Based Approaches for Distributed Data Mining Applications.
Nhien Le Khac,Lamine Aouad,M-T Kechadi (2010). Toward Distributed Knowledge Discovery on Grid Systems.
Eshref Januzaj,Hans-Peter Kriegel,Martin Pfeifle (2004). DBDC: Density Based Distributed Clustering.
N-A Le-Khac,L-M Aouad,M-T Kechadi (2007). A new approach for distributed density based clustering on grid platform.
Lamine Aouad,Nhien-An Le-Khac,Tahar Kechadi (2007). Lightweight Clustering Technique for Distributed Data Mining Applications.
Lamine Aouad,Nhien-An Le-Khac,Tahar Kechadi (2007). Lightweight Clustering Technique for Distributed Data Mining Applications.
Jiawei Han,Micheline Kamber,Jian Pei (2006). Advanced Pattern Mining.
Jean-Francois Laloux,Nhien-An Le-Khac,M-T Kechadi (2011). Efficient Distributed Approach for Density-Based Clustering.
M Bertolotto,S Di Martino,F Ferrucci,T Kechadi (2007). Towards a framework for mining and analysing spatio‐temporal datasets.
Philip Chan,Salvatore Stolfo (1995). A Comparative Evaluation of Voting and Meta-learning on Partitioned Data.
C Reeves (1993). Modern heuristic techniques for combinatorial problems.
Lamine Aouad,Nhien-An Le-Khac,Tahar Kechadi (2009). Performance study of distributed Apriori-like frequent itemsets mining.
Inderjit Dhillon,Dharmendra Modha (1999). A Data-Clustering Algorithm on Distributed Memory Multiprocessors.
M Ester,H Kriegel,J Sander,X Xu (1996). A density-based algorithm for discovering clusters in large spatial databases with noise.
Ashwani Garg,Ashish Mangla,Neelima Gupta,Vasudha Bhatnagar (2006). PBIRCH: A Scalable Parallel Clustering algorithm for Incremental Data.
H Geng,X Deng (2005). A new clustering algorithm using message passing and its applications in analyzing microarray data.
Inderjit Dhillon,Dharmendra Modha (2000). A Data-Clustering Algorithm on Distributed Memory Multiprocessors.
Xiaowei Xu,Jochen Jäger,Hans-Peter Kriegel (1999). A Fast Parallel Clustering Algorithm for Large Spatial Databases.
Nhien-An Le-Khac,Martin Bue,Michael Whelan,M Kechadi (2010). A Clustering-Based Data Reduction for Very Large Spatio-Temporal Datasets.
J Fadili,M Melkemi,A Elmoataz (2004). Nonconvex onion-peeling using a shape hull algorithm.
A Chaudhuri,B Chaudhuri,S Parui (1997). A Novel Approach to Computation of the Shape of a Dot Pattern and Extraction of Its Perceptual Border.
Mahmoud Melkemi,Mourad Djebali (2000). Computing the shape of a planar points set.
H Edelsbrunner,D Kirkpatrick,R Seidel (1983). On the shape of a set of points in the plane.
A Moreira,M Santos (2007). CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION OCCUPIED BY A SET OF POINTS.
Matt Duckham,Lars Kulik,Mike Worboys,Antony Galton (2008). Efficient generation of simple polygons for characterizing the shape of a set of points in the plane.
Tian Zhang,Raghu Ramakrishnan,Miron Livny (1996). BIRCH.
Sudipto Guha,Rajeev Rastogi,Kyuseok Shim (2001). Cure: an efficient clustering algorithm for large databases.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

K Lakshmaiah. 2018. \u201cAn Extended Linked Clustering Algorithms for Spatial Data Sets\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 18 (GJCST Volume 18 Issue C3): .

More Citation Formats

Select Citation Style:

Download Citation

Download Article

GJCST Volume 18 Issue C3
Pg. 37- 44

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Not Found

Classification

GJCST-C Classification: I.5.3

Submission ReceivedDecember 6, 2017
Peer Review Double Blind
Handling Editor
Accepted January 4, 2018
Published January 15, 2018

Version of record

v1.2

Issue date

July 27, 2018

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: K Lakshmaiah ,Research Scholar. (PhD/Dr. count: 0)

View Count (all-time): 276

Total Views (Real + Logic): 5961

Total Downloads (simulated): 1604

Publish Date: 2018 07, Fri

Monthly Totals (Real + Logic):

Month 1: 28 views
Month 2: 54 views
Month 3: 48 views
Month 4: 31 views
Month 5: 39 views
Month 6: 49 views
Month 7: 16 views
Month 8: 50 views
Month 9: 24 views
Month 10: 30 views
Month 11: 26 views
Month 12: 30 views
Month 13: 40 views
Month 14: 14 views
Month 15: 39 views
Month 16: 17 views
Month 17: 21 views
Month 18: 20 views
Month 19: 25 views
Month 20: 30 views
Month 21: 50 views
Month 22: 21 views
Month 23: 36 views
Month 24: 31 views
Month 25: 29 views
Month 26: 35 views
Month 27: 41 views
Month 28: 28 views
Month 29: 27 views
Month 30: 26 views
Month 31: 41 views
Month 32: 43 views
Month 33: 19 views
Month 34: 35 views
Month 35: 22 views
Month 36: 31 views
Month 37: 38 views
Month 38: 32 views
Month 39: 19 views
Month 40: 34 views
Month 41: 37 views
Month 42: 41 views
Month 43: 27 views
Month 44: 13 views
Month 45: 40 views
Month 46: 38 views
Month 47: 31 views
Month 48: 29 views
Month 49: 41 views
Month 50: 24 views
Month 51: 26 views
Month 52: 34 views
Month 53: 39 views
Month 54: 45 views
Month 55: 36 views
Month 56: 21 views
Month 57: 35 views
Month 58: 31 views
Month 59: 13 views
Month 60: 24 views
Month 61: 23 views
Month 62: 42 views
Month 63: 21 views
Month 64: 27 views
Month 65: 38 views
Month 66: 34 views
Month 67: 43 views
Month 68: 25 views
Month 69: 21 views
Month 70: 33 views
Month 71: 20 views
Month 72: 21 views
Month 73: 18 views
Month 74: 22 views
Month 75: 23 views
Month 76: 22 views
Month 77: 37 views
Month 78: 28 views
Month 79: 36 views
Month 80: 38 views
Month 81: 32 views
Month 82: 23 views
Month 83: 39 views
Month 84: 28 views
Month 85: 40 views
Month 86: 46 views
Month 87: 22 views
Month 88: 25 views
Month 89: 42 views
Month 90: 31 views
Month 91: 85 views
Month 92: 48 views
Month 93: 47 views

Total Views: 5961

Total Downloads: 1604

2026 Trends

Published Article

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]