Application of k-means Clustering to Multidimensional Geo-spatial Data and Clustering Algorithm for Handling Data Precision |
Author(s): |
| Kiran M N , Dayananda Sagar College Of Engineering; Dhanjai N, Dayananda Sagar College Of Engineering; Mahendra Kumar .B, Dayananda Sagar College Of Engineering |
Keywords: |
| K-Means Clustering, Privacy Preserving, Anonymized Table, AES Encryption Algorithm, Multi-Spectral Data, Geo-Spatial Data, Machine Learning, Active Learning |
Abstract |
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Clustering is a method of grouping a group of objects in such a way that data objects in same group called "CLUSTER" have similar properties to each other than to those in other cluster (group). The K-means algorithm is based on simple idea i.e. Given a set of initial clusters points, assign each point to one of items, then each cluster center is replaced by the mean point on the respective cluster. These two simple steps are repeated until convergence. A point is assigned to the cluster which is close in Euclidean distance to the point. In this paper we will discuss how K-means algorithm is used for clustering of data (objects) and solve problems like active learning intrusion detection, distribution of teachers, clustering of multidimensional geo-spatial data and handling data precision. |
Other Details |
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Paper ID: IJSRDV7I40778 Published in: Volume : 7, Issue : 4 Publication Date: 01/07/2019 Page(s): 1168-1173 |
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