Implementation of DBSCAN Algorithm using Similarity Measure from Rapid Miner

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Provided by: International Journal of Applied Information Systems (IJAIS)
Topic: Big Data
Format: PDF
The DBSCAN is density fundamental cluster formation. Its advantage is that it can discover clusters with arbitrary shapes and size. Clustering methods can be categorized into two main types: fuzzy clustering and hard clustering. In fuzzy clustering, data points can belong to more than one cluster with probabilities. In hard clustering, data points are divided into distinct clusters, where each data point can belong to one and only one cluster. In this paper, the authors have calculated similarity measure in rapid miner.
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