A New Improved Apriori Algorithm For Association Rules Mining
Girja Shankar, Latita Bargadiya · 2013
In today's world of competitive business environment and popularization of computer and development of database, more and more data are stored in the large database. There is a great need to extract hidden and potentially meaningful information from large database. It is impossible to find useful information with traditional method. And then, data mining techniques have come as a reflection of this problem. Association rule mining is an important research area in data mining field. Is aim to find out the remarkable association or relationship between a large set of data items. Data mining techniques are very easy to handle. It is very easy to implement this technique on the existing software and hardware platforms to improve the quality of the information resources. It can also be integrated with new techniques and systems which are newly introduced in the market. Association rule mining finds interesting associations and/or correlation relationships among large set of data items. Association rules show attributes value conditions that occur frequently together in a given dataset. Association rules provide information of this type in the form of if-then statements. These rules are computed from the data and, unlike the ifthen rules of logic, association rules are probabilistic in nature. In addition to the antecedent (the if part) and the consequent (the then part), an association rule has two numbers that express the degree of uncertainty about the rule. In association analysis the antecedent and consequent are sets of items (called item sets) that are disjoint (do not have any items in common). Support: The support is simply the number of transactions that include all items in the antecedent and consequent parts of the rule. (The support is sometimes expressed as a percentage of the total number of records in the database.) Confidence: Confidence is the ratio of the number of transactions that include all items in the consequent as well as the antecedent (namely, the support) to the number of transactions that include all items in the antecedent. Lift: Lift is nothing but the ratio of confidence to expected confidence. Lift is a value that gives us information about the increase in probability of the then (consequent) given the if (antecedent) part.