Mining Rare Association Rule

Pattern mining is a data mining method that involves finding existing patterns in data. The main objective is to uncover the hidden regularities in the data. Until now most of the research in pattern mining has been exclusively focused on frequent item set, however in some situation it is interesting to find patterns that are rare instead of frequent or patterns that reflect a negative co relation between items. These patterns are referred to as rare and negative patterns. For example in jewellery sales data, sales of diamond watches are rare however patterns involving the selling of diamond watches could be interesting. Rare items occur rarely but are of special interest as they may highlight exceptional behaviour in the data which is likely of interest. It tries to capture patterns that involve events that are unusual in the data set. This research proposes that in some domain such as medical databases, computer security, rare items are of higher importance than frequent one. This research investigates other kind of knowledge that is not usually discovered using frequent item-set mining i.e. rare item-sets and non-present item-sets. In order to mine rare and non-present item-sets, we methodologically extract the principles behind Apriori approach, and then generalize it in such a way it can not only be applied to mine frequent item-sets but also to mine other categories of patterns such as rare and non-present item-sets. Our goal is to apply the principle of the Apriori approach used for frequent item-set mining and apply it to rare and nonpresent item-set mining. The proposed approach is able to find the whole rare item set and at the same time pruning out the zero itemset.We will be using the concept of minimal rare item set (MRI) to generate strong Rare Association rules with high confidence and low support. The MRI will act as a generator, based on which valid rare association rule will be generated Keywords— Rare item set, Minimal Rare itemset, Rare Association Rule. INTRODUCTION Data mining techniques are used to process huge amounts of information in order to extract hidden knowledge to be directly interpreted or exploited to feed other processes. In many cases, data mining techniques are used to discover patterns that can be of interest to a specific domain of application. A pattern is a collection of events/features that occur together in a transaction database. As a matter of fact, different categories of patterns exist, such as sequences, item-sets, association rules and graph patterns, etc. The choice of a technique depends not only on the nature of input data but also depends on what we want to obtain and what view or part of the data we want to be described or represented in a more intelligible and concise way. To filter out the patterns, some criteria are used. The most known criteria are the support and the confidence. While the support represents the number of times a pattern occurs in the initial database, the confidence represents a proportion value that shows how frequently a part of the pattern, called premise, occurs among all the records containing the whole rule body. Herein, we classify pattern categories according to the specific use of the support threshold. In fact, by setting ranges for the support, one can obtain different categories of patterns. For example, if we set a minimum support threshold that a pattern has to satisfy to be considered as an interesting pattern, we obtain what is called frequent pattern. By setting a maximum support threshold we obtain another category of patterns called rare patterns. Whereas frequent patterns focus on mining patterns that appear more frequently in the database, rare patterns aim at discovering patterns that are less frequent. Rare patterns can be used in different domains such as biology, medicine and security etc. For example, by analyzing clinical databases one can discover rare patterns that will help doctors to make decisions about the clinical care. In the security field, normal behavior is very frequent, whereas abnormal or suspicious behavior is less frequent. Considering a database where the behavior of people in sensitive places such as airports are recorded, if we model those behaviors, it is likely to find that normal behaviors can be represented by frequent patterns and suspicious behaviors by rare patterns. BASIC CONCEPTS An association rule is an expression of the form P1 → P2, where P1 and P2 are arbitrary item sets (P1, P2 ⊆ A), P1 ∩ P2 = ∅ and P2 = ∅. The left side, P1 is called antecedent, the right side, P2 is called consequent. The support of an association rule r: P1 → P2 is defined as: supp(r) = supp (P1 ∪ P2). The confidence of an association rule r: P1 → P2 is defined as the conditional probability that an object includes P2, given that it includes P1: conf(r) = supp(P1 ∪P2)/supp(P1). An association rule r is called confident, if its confidence is not less than a given minimum confidence (denoted by min_conf), i.e. conf(r) ≥ min_conf. An association rule r with conf(r) = 1.0 (i.e. 100%) is an exact association rule; otherwise it is an approximate association rule. An association rule r is called frequent if its support is not less than a given minimum support (denoted by min_supp), i.e. supp(r) ≥ min_supp. A frequent association rule is valid if it is confident, i.e. supp(r) ≥ min_supp and conf(r) ≥min_conf. An association rule is called rare if its support is less than a given minimum support. A rare association rule r is valid if r is confident, i.e. supp(r) < min_supp and conf(r) ≥ min_conf. Let us consider the standard pattern mining environment, i.e., a set of m items I = {i1, i2, ・ ・ ・ , im} and a transaction database (TDB) D = {t1, t2, ・ ・ ・, tn} on top Arnab kumar Das / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (6) , 2015, 5552-5557