Data mining is the process of sorting out data in order to identify their patterns and to establish their relationships. Data mining deals with the use of complex data analysis tools to unravel previously unknown patterns and relationships in huge amounts of data sets. The tools used are mainly made up of models of statistics, mathematical algorithms and other machine learning methods. The process of data mining is done in a sequence of activities which include data collection, management of data, analysis and prediction. Data mining is becoming an increasingly important tool used in the conversion of data into information in various areas in which its applicable. Most industries use data mining to cut down their expenditures, promote innovative activities that may help in improving their businesses like research and also to boost the industrial productivity. For example, industries may use data mining to determine malicious cases in thei
r firms like theft. Additionally, this process is also used to assess risks in organizations. In most cases data mining can be used to reveal data patterns although this can only be performed only on data samples. However, this whole process does not work properly or can fail in cases where the samples used do not reflect the whole population of the original data. Therefore, the data mining techniques in use may not bring out data patterns which are more likely to be in the original sample of data if the same patterns are not found in the specific sample that is used in the mining process. In addition, the invention of certain data patterns in a specific set of data does not actually mean that the same pattern can be traced in the larger data from where that sample was obtained from. For this reason, an imperative part of the procedure is the confirmation and substantiation of patterns on supplementary samples of data (Seifert 2004, p.1).