AN EMINENT WAY OF AN IMPROVING A DENCLUE ALGORITHM APPROACH FOR OUTLIER MINING IN LARGE DATABASE
DOI:
https://doi.org/10.26438/ijcse/v6i10.536540Keywords:
Clustering, Data Mining, Density Based Clustering Algorithm, DBSCAN, OPTICS, Outlier MiningAbstract
The number of methods available in data mining to detect the outlier by making the clusters of data and then detect the outlier from them. The objects that are similar to each other are organized in group it’s called cluster and the objects that do not comply with the model or general behavior of the data these data objects called outliers. Outliers detect by clustering. Density based clustering algorithm (DENCLUE) is one of the primary methods for clustering in data mining which groups neighboring objects into clusters based on local density conditions rather than proximity between objects. Data points are assigned to a cluster by hill climbing, points going to the same local maximum are put into the same cluster. The traditional density estimation only considers the location of the point, not variable of interest. Depending on the convergence criteria, the method needs less iteration as fixed step size methods and improving cluster quality and also finding an outlier correctly.
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