SNN Input Parameters: How Are They Related?

Abstract

Nowadays, organizations are facing several challenges when they try to analyze generated data with the aim of extracting useful information. This analytical capacity needs to be enhanced with tools capable of dealing with big data sets without making the analytical process a difficult task. Clustering is usually used, as this technique does not require any prior knowledge about the data. However, clustering algorithms usually require one or more input parameters that influence the clustering process and the results that can be obtained. This work analyses the relation between the three input parameters of the SNN (Shared Nearest Neighbor) algorithm and proposes specific guidelines for the identification of the appropriate input parameters that optimizes the processing time.

Understanding the SNN Input Parameters and How They Affect the Clustering Results

“…In the set of the three input parameters, k is the most difficult one to estimate. After testing also several random data sets extracted from the original data sets, t4.8k, t5.8k, t7.10k, t8.8k, with different number of points, 4.000, 5.000, 6.000 and 7.000, it was possible to verify that k is usually contained in an interval that ranges from 0,70% and 1% of the size of the data set (G. Moreira, Santos, & Moura-Pires, 2013). Table 2 shows the estimated range for k using the proposed heuristics, as well as the value of k, classified as excellent, obtained after processing and analyzing all the clustering results.…”