Frequent item set mining and association rule mining is the key tasks in knowledge discovery process. Various customized algorithms are being implemented in Association Rule Mining process to find the set of frequent patterns. Though we have many algorithms apriori is one of the standard algorithm for finding frequent itemsets, but this algorithm is inefficient because of several scans of database and more number of candidates to be generated. To overcome these limitations, in this paper a new algorithm called Coalesce based Binary Table is introduced. Through this algorithm the given database is scanned only once to generate Binary Table by which frequent-1 itemsets are found. To progress the process, infrequent-1 itemsets are identified and removed from the Binary Table to rearrange the items in support ascending order. To each frequent-1 itemset find Coalesce matrix and Index List to generate all frequent itemsets having the same support count as representative items and the remaining frequent itemsets are obtained in depth first manner. The significant benefits with the proposed method are the whole database is scanned only once, no need to generate and check each candidate to find the set of frequent items. On the other hand frequent items having the same support counts as representative items can be identified directly by joining the representative item with all the combinations of Coalesce matrix. So, it is proven that coalesce based Binary Table is panacea to cut short the time in identifying the frequent itemsets hence the efficiency is improved.
Today health care services have come up with an advanced way to treat patients having different diseases. Among all, one of the harmful diseases is the cardiovascular disease that can't be visible with a unadorned eye and comes right away when its limitations are reached. With rise in population, there is a rise in heart disease rate. Today, diagnosing patients in an effective manner have become a challenging task. The healthcare industry picks up large quantity of healthcare data but, rarely that is used to extract hidden patterns for efficient decision making purpose. Thus, we proposed to develop an approach which will help practitioners to diagnosis heart related disease. So, there is a necessity to develop a decision making system which will helps practitioners to predict heart diseases in an easier way and will offer automated predictions about the condition of the patient's heart so that further treatment can be done effectively. This proposed system will not only make accurate predictions about heart disease but also brings down cost & time. The Machine Learning algorithms have determined to be most accurate & reliable and hence used in this paper.
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