Abstract: Mining of frequent item sets is one of the most fundamental problems in data mining applications. My proposed algorithm which guides the doctor to select the symptoms of a new patients , due to testing constraints there is a limit, say m, on the number of symptoms that can be selected for the identify the covid-19 disease. Although the problems are NP complete. The Approximation algorithm is based on greedy heuristics. DCIP algorithm uses data-set condensing and intersection pruning to find the maximal frequent symptoms. This algorithm differs from all classical maximal frequent item set discovering algorithms; experiments show that this algorithm is valid with moderate efficiency; it is also easy to code for use in KDD applications.
Keywords: Association rules, Data mining, Mining frequent item sets, intersection pruning, and data-set condensing
| DOI: 10.17148/IARJSET.2020.7419