Loading...
Please wait, while we are loading the content...
Similar Documents
Emerging Trends in Associative Classification Data Mining
| Content Provider | Semantic Scholar |
|---|---|
| Author | Abdelhamid, Neda Ayesh, Aladdin Ebusiness, Fadi Thabtah |
| Copyright Year | 2014 |
| Abstract | Utilising association rule discovery to learn classifiers in data mining is known as Associative Classification (AC). In the last decade, AC algorithms proved to be effective in devising high accurate classification systems from various types of supervised data sets. Yet, there are new emerging trends and that can further enhance the performance of current AC methods or necessitate the development of new methods. This paper sheds the light on four possible new research trends within AC that could enhance the predictive performance of the classifier or their quality in terms of rules. These possible research directions are considered starting research points for other scholars in rule based classification in data mining. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijeee.net/uploadfile/2014/0507/20140507041500642.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Association rule learning Care-of address Data mining Decision Trees Decision tree Rule (guideline) Rule 90 Rule induction Scientific literature Supervised learning Test data Trees (plant) disease classification |
| Content Type | Text |
| Resource Type | Article |
