Abstract
Data extraction, data processing, pattern mining and clustering are the important features in data mining. The extraction of data and formation of interesting patterns from huge datasets can be used in prediction and decision making for further analysis. This improves, the need for efficient and effective analysis methods to make use of this data. Clustering is one important technique in data mining. In clustering a set of items are divided into several clusters where inter-cluster similarity is minimized and intra-cluster similarity is maximized. Clustering techniques are easy to identify of class in large databases. However, the application to large databases rises the following requirements for clustering techniques: minimal requirements of domain knowledge to determine the input specifications, invention of clusters with absolute shape & certainty of large databases.. The existing clustering techniques offer no solution to the combination of requirements. The proposed clustering technique DBSCAN using KNN relying on a density-based notion of clusters which is accomplished to discover clusters of arbitrary shape.
Highlights
Processing of huge data is very complicated task in the present world
In this paper, proposed clustering technique DBSCAN using KNN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape
In clustering it is very important to know that the formation of similar data points as a cluster should be meaningful.The proposed density based clustering accepts the finding of arbitrary shape and no need of convex areas of data points that are more generated
Summary
Processing of huge data is very complicated task in the present world. Many users want to store or represent the huge information as data. In clustering it is very important to know that the formation of similar data points as a cluster should be meaningful.The proposed density based clustering accepts the finding of arbitrary shape and no need of convex areas of data points that are more generated. Many of the clustering algorithms require that the quantity of groups is to be known proceeding the begin of clustering process others decide the clusters themselves as a rule Density-based clustering algorithms are free of earlier learning of a number of a clusters. Such algorithms might be helpful in circumstances where the quantity of cluster is to be resolved effortlessly before the begin of the algorithm
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