학술논문

Clustering centroid finding algorithm (CCFA) using spatial temporal data mining concept
Document Type
Conference
Source
2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on. :30-36 Feb, 2013
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Signal Processing and Analysis
Clustering algorithms
Hurricanes
Data mining
Spatial databases
Tropical cyclones
Spatial Temporal
Hurricane Dataset
Clustering window
Centroid Points
Average Centroid Values
Language
Abstract
The main aim of the research focuses the clustering centroid value for spatio-temporal data mining. Using k-means, advanced k-means algorithm and Avg Centroid (AC) clustering. The real time data of the hurricane Indian Ocean 2001 to 2010 maximum wind details are focused in this paper. The clustering is taking as selection window method, the first window form the basis of the pixel coordinate value of the screen, the second clustering window one half of the centre point value. The data mining retrieves clustering data form basis of the selection window. Here to discuss k-means algorithmic steps are very few and same iteration is continuing till the same to get the centroid point. The enhanced k-means algorithm taken more steps but result is accurate algorithmic finishing stage; iteration also repeated very minimum times. The final discussion of this paper collects average centroid clustering for all previously selected values and current selected clustering data. The result of this paper gave the comparative study of the k-means, enhanced k-means algorithms and AC clustering values.