@article{503,
  author = {Rose Tinabo},
  title = {A Mechanism for Selecting Appropriate Data Mining Techniques},
  journal = {Journal of Intelligent Computing},
  year = {2011},
  volume = {2},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jic/fulltext/v2n1/4.pdf},
  abstract = {Due to an increase of several data mining techniques, selection of an appropriate data mining technique to use is one of the important steps when undertaking a data mining project. Data miners used to test various numbers of techniques to work out which one will give them the best results every time they have new data, this is costly and time consuming. They can solve this problem by understanding the main objective of mining, strength and weaknesses of the techniques and the features of the data they have, to determine the best technique to use without testing numbers of techniques. Testing different techniques shows that, there is a confusion of what data mining technique will be most appropriate for them than other techniques. Therefore, this paper discus different strength and weaknesses of four different data mining techniques and compare their performance on different features of the datasets by four different evaluation metrics. The summary was theoretically applied to the retail dataset to decide on the most appropriate technique for customer retention as a case study. Thus, users can use the summary provided in this paper to select the most appropriate data mining technique.},
}