Problem Spaces and Algorithms in Data Mining
Publication Date: 02/01/2024
Author(s): Ele B. I., Obono I. O., Iwinosa A. A..
Volume/Issue: Volume 7 , Issue 1 (2024)
Abstract:
Data mining has been described severally as the best thing to have happened to data and information management, especially these days that the cost of computing technologies and storage media are falling, data gathering tools becoming varied and very efficient and the boom in network computing becoming very rewarding. The challenges presented by the management and meaningful usage of large data sets have stimulated so much research in data mining. Consequently, the birth of a number of algorithms to provide insights to these big data has equally presented more complications in information processing computing. Therefore, this paper presents different problem spaces in data mining, available algorithms to mine these data and then mapping specific algorithms to specific problem spaces. Analysis of datasets from a typical financial institution suggests that no one algorithm is necessarily better than the other, but all have strengths and weaknesses depending on the particular problem spaces in use.
Keywords:
Data mining, Problem spaces, Data analytics, Big data, Algorithms.
