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II. RELATED WORK To discover association rules, existing data-mining algorithms [23] require the domains of quantitative attributes to be discretized into intervals. The idea has been proposed in [23] to use equidepth partitioning for optimizing a partial completeness measure so that the intervals are neither too big nor too small with respect to the set of association rules that are discovered by their data-mining algorithm. {GOOGLEADS}Regardless of how the values of the quantitative attributes are discretized, the intervals might not be concise and meaningful enough for human users to easily obtain nontrivial knowledge from the discovered association rules. Linguistic summaries, which were introduced in [25], express knowledge using a linguistic representation that is natural for human users to comprehend. An example of a linguistic summary is the statement, “about half of the people in the database are middle aged.” Unfortunately, no algorithm was proposed for generating the linguistic summaries in [25]. Recently, the use of an algorithm for mining association rules for the purpose of linguistic summaries has been studied in [14]. This technique extends AprioriTid [2], which is a well-known algorithm for mining association rules, to handle linguistic terms (fuzzy values). An attribute is replaced by a set of artificial attributes (items) so that a tuple supports a specific item to a certain degree, which is in the range 0 to 1. Given two user-specified thresholds, and , an item or an itemset (i.e., a combination of items) is considered interesting if its fuzzy support is greater than and it is also less than . Although this technique is very useful, many users may not be able to set the thresholds appropriately.

In addition to the linguistic summaries, an interactive process for the discovery of top-down summaries, which utilizes fuzzy is-a hierarchies as domain knowledge, has been described in [15]. This technique is aimed at discovering a set of generalized tuples, such as technical writer, documentation . In contrast to association rules, which involve implications between different attributes, the generalized tuples only provide summarization on different attributes. The idea of implication has not been taken into consideration and hence these techniques are not developed for the task of rule discovery. Furthermore, the applicability of fuzzy modeling techniques to data mining has been discussed in [13]. Given a relational table, and a context variable, , the context-sensitive fuzzy clustering method is aimed at revealing the structure in in the context of . Since this method can only manipulate quantitative attributes, the values of any categorical attributes are first

The bank-account database was provided by a bank in Hong Kong. The bank does not want to be identified in our paper because customer attrition rates are confidential. The bank-account database is stored in an Oracle database, which is one of the most popular relational database management systems [9]. It is composed of three relations, namely, CUSTOMER, ACCOUNT, and TRANSACTION. Of these relations, CUSTOMER and ACCOUNT contain FREE BANKING BOOKS relational data, whereas TRANSACTION contains transactional data. Specifically, the bank maintains a tuple in CUSTOMER for each customer (e.g., sex, age, marital status, etc.), a tuple in ACCOUNT for each account owned by a customer (e.g., account type, loan amount limit, etc.) and a tuple in FREE BANKING BOOKS PDF TRANSACTION for each transaction made by a customer on one of his/her accounts (e.g., cash deposit, cash withdrawal FREE BANKING STUDY MATERIALS, etc.). A customer can have one or more accounts and an account can have one or more transactions. Accordingly, a BANKING BOOKS 2017-2018 tuple in CUSTOMER is associated with one or more tuples in ACCOUNT and a tuple in FREE BANKING BOOKS DOWNLOAD PDF ACCOUNT BANKING STUDY MATERAILS is associated with one or more tuples in TRANSACTION. Fig. 1 shows the schema of the bank-account database. Since each relation in the bank- EXAM PREPARATION & TUTORINGaccount database contains many at- tributes, we only show a subset of these attributes in Fig.{GOOGLEADS} 1. It is important to note that a relation in a relational database may contain relational data or transactional data. The entity that a relation represents is what makes it either relational or transac- tional. In a relation that contains transactional data, each tuple (transaction record) FREE DOWNLOAD FOR PDF represents a business transaction. Specifically, a transaction record represents a debit or credit transaction