Intelligent Methods of Fusing the Knowledge During Incremental Learning via Clustering in A Distributed Environment
Panduranga Naidu Nagabhushan, Syed Zakir Ali, R. Pradeep Kumar · International Journal of Advanced Networking and Applications · 2010
One of the ways of learning from the data which is physically distributed over multiple locations is to have a common learning mechanism at each of the source and knowledge of each of the learnt concepts has to be transmitted to a centralized location for assimilation. In this research, clustering is employed as a mechanism of learning and a cluster is viewed as a concept which is described by a set of variables. The set of variables which describes each of the clusters is being referred to as a knowledge packet (KP) . As histograms have the generic ability to characterize any type of data, a histogram based regression line has been used as one of the variable to describe a KP . For online monitoring of the progression in learning apart from achieving computational ease and efficacy, the KP s at the centralized location are fused incrementally to get the overall knowledge. If learning mechanisms employed are data sequence sensitive, different combinations of merging the thus generated KP s may result in altogether a different overall knowledge. Further, the distance measure employed to find distance between the KP s in obtaining the optimal sequence of merging, may also result in a different overall knowledge. This phenomenon is being referred to as the problem of order effect . To minimize or avoid the order effect , a density based spatial clustering of applications with noise (DBSCAN) algorithm, which is insensitive to the order of presentation of data samples is used to learn from the data chunks and a novel methodology of finding the distance between the batches of data and there by finding the more optimal sequence of merging the KP s is presented. A specially designed distance measure for histogram based objects (histo-objects) is employed to find distance between the KP s and the nearest KP s are merged incrementally till certain conditions are satisfied. The proposed methods provide a robust mechanism of avoiding order effects . Since it is difficult to get the real distributed datasets, effectiveness of the proposed approaches is demonstrated with a carefully designed synthetic dataset. Some of the bench mark datasets were modified to simulate the distributed environment and experimentations with some of them show an accuracy of up to 100%.