Analysis of data mining evaluation methods’ efficiency
International Journal of Development Research
Analysis of data mining evaluation methods’ efficiency
Received 18th August 2017; Received in revised form 09th September, 2017; Accepted 14th October, 2017; Published online 29th November, 2017
Copyright ©2017, Roumen Trifonov et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
After the data mining algorithm has concluded, the results have to be evaluated. The evaluation will show the used algorithm’s accuracy. The main goal of data mining is to discover hidden patterns in data sets. To achieve this there are different algorithms or methods. The algorithms have to be compared based on the work they have performed on the data sets. There are several statistical-based tests to verify that the differences between the methods are not due to some chance effect. Each machine learning technique, used in data mining has different performance in terms of a specific problem. The problem is associated with data set. This article will represent a general overview of the performance measuring techniques.
