Author : Ch. Mallikarjuna Rao 1
Date of Publication :7th March 2016
Abstract: Segmentation Based Clustering has been accepted widely as a novel method for analysing the Spatial Data. Many types of Modern Global Positioning Systems (GPS) and also other data acquisition mechanisms are widely used for collecting huge amount of geographical data , which is expected to grow exponentially. It is observed that Mining of such huge data can extractunknown and latent information from spatial datasets that are characterized by complexity, dimensionality and large size. However, it is challenging to do so.Geographical knowledge discovery through spatial data mining has emerged as an attractive field that provides methods to leverage useful applications. Remote sensing imagery is the rich source of geographical data. Analysing such data can provide actionable knowledge for making strategic decisions. This paper proposes a Novel methodology that is used to perform clustering on remote sensing data. Thesedata sets are collected and used World Wind application, provided by NASA. The images are with .TIF extension. The methodology includes feature extraction, training, building classifier and cluster analysis. We built a prototype application that demonstrates the proof of concept. The implementation has taken native method support from Fiji and Weka to realize the proposed methodology. The empirical results revealed that the spatial clustering is made with high accuracy.
Reference :
-
- Wei Wang, Jiong Yang, and Richard Muntz (1997). STING : A StatisticalInformationGrid Approach to Spatial Data, p.23-37.
- JOHANNES GRABMEIER, ANDREAS RUDOLPH(2002). Techniques of Cluster Algorithms in Data Mining. and Knowledge Discovery, p.12-20
- Daniel A. Keim, Florian Mansmann, J¨orn Schneidewind, and Hartmut Ziegler(2006).Challenges in Visual Data Analysis.IEEE, p.45-55.
- ZHEXUE HUANG(1998).Extensions to the Means Algorithm for ClusteringLarge Data Sets with Categorical Values.HUANG p.32-42.
- Stephen M. Smith, Mark Jenkinson,a Heidi JohansenBerg, Daniel Rueckert,Thomas E. Nichols, Clare E. Mackay, Kate E. Watkins,Olga Ciccarelli, M. Zaheer Cader,Paul M. Matthews, and Timothy E.J. Behrens(2006). Tract-based spatial statistics:Voxelwise analysis of multi-subject diffusion data. Elsevier, p.25-35.
- James A. Wise, James J. Thomas, Kelly Pennock, David Lantrip,Marc Pottier, Anne Schur, Vern Crow(1995). Visualizing the Non-Visual: Spatial analysis and interactionwith information from text documents.IEEE, p.45-55.
- Martin Ester, Hans-Peter Kriegel, Jtirg Sander(1997).Spatial Data Mining: A Database Approach, p.12-19.
- DW van der Merwe, AP Engelhrecht(2003). Data Clustering using Particle Swarm Optimization, p.34- 45.
- Raymond T. Ng and Jiawei Han(2002). CLARANS: A Method for Clustering Objectsfor Spatial Data Mining.IEEE,p.45-56.
- J.-F. Mari · F. Le Ber(2006). Temporal and spatial data mining with second-orderhidden markov models. Springer-Verlag 2005, p.23-34.
- Daniel A. Keim, Christian Panse, Mike Sips, Stephen C. North(2004). Pixel based visual data miningof geospatial data. Elsevier p.1-17.
- Julia Handl., Joshua Knowles and Douglas B. Kell(20054). Computational cluster validation in postgenomic data analysis, p.23-33.
- Anthony K. H. Tung Jean Hou Jiawei Han(2001).Spatial Clustering in the Presence of Obstacles.IEEE, p.34-44.
- X.M•DYang a, W. Cui b, J.M. Gong a, T. Zhang (2005). INFORMATION MINING FROM REMOTE SENSING IMAGERYBASED ON MULTI-SCALE AND MULTI-FEATURE PROCESSING TECHNIQUES, p.120-135.
- Leen-Kiat Soh, Costas Tsatsoulis (1999). Segmentation of Satellite Imagery of Natural
- Scenes Using Data Mining.CSE Journal Articles, p.23- 34.