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BANKING STUDY MATERAILS - MINING FUZZY ASSOCATION RULES IN A BANK ACCOUNT DATABASE BY WAI HO AU AND KEITH CC CHAN

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This rule states that a customer who buys pizza and chicken FREE DOWNLOAD FOR PDF wings also buys coke and salad. {GOOGLEADS}Although the existing algorithms for mining association rules (e.g., [23]) can be used to identify interesting characteristics of different types of bank customers, they require the domains of the quantitative attributes to be discretized into intervals. These intervals are often difficult to define. In addition, if EXAM PREPARATION & TUTORING too much data lies on the boundaries of the intervals, then this could result in very different discoveries in the data that could be both misleading and meaningless. In addition to the need for discretization, there is a requirement for users to provide the thresholds for minimum support and confidence and this also makes the existing techniques BANKING STUDY MATERAILS difficult to use (e.g., [1], [2], [18], [21], and [23]). If the thresholds are set too high, a user may miss some useful rules, but if the thresholds are set too low, the user may be overwhelmed by too many irrelevant rules [11]. To handle the problems that were given to us by the banking officials, we developed a fuzzy technique for data mining that is called the Fuzzy Association Rule Mining II (FARM II). FARM II employs linguistic terms to represent the revealed regularities and exceptions. This linguistic representation is especially useful when the discovered rules are presented to human experts for examination because of its affinity with human knowledge representation. Since our interpretation of the linguistic terms is based on fuzzy-set theory, the association rules that are expressed in these terms are referred to hereinafter as fuzzy association rules [3]–[6]. An example of a fuzzy association rule is given as follows:

association rule involving discrete intervals, the fuzzy association rule is easier for human users to comprehend. In addition to the linguistic representation, the use of fuzzy techniques hides the boundaries of the adjacent intervals of the quantitative attributes. This makes FARM II resilient to noise in the data, such as inaccuracies in the physical measurements of real-life entities. Furthermore, the fact that 0.5 is the fuzziest degree of BANKING BOOKS 2017-2018 membership of an element in a fuzzy set provides a new means for FARM II to deal with missing values in databases. Using defuzzification techniques, FARM II allows quantitative values to be inferred when fuzzy association rules are applied to as yet unseen records. To avoid the need for user-specified thresholds, FARM II utilizes an objective interestingness measure, which is defined in terms of a fuzzy support and confidence measure [3]–[7] that reflects the actual and expected degrees to which a tuple is characterized by different linguistic terms. Unlike other data-mining algorithms (e.g., [1], [2], [18], [21], and [23]), the use of this in- terestingness measure has the advantage that it does not require any user-specified thresholds. In addition to dealing with fuzzy data and using an objective interestingness measure, the technique also needs to deal with the problem that is created by the fact that there is more than one database relation. In such a case, the concept of a universal relation needs to be used. A universal relation is FREE BANKING STUDY MATERIALS an imaginary relation that can be used to represent the data that is constructed by logically joining all of the separate tables of a relational database [24]. The use of a universal relation, therefore, makes it possible for the existing data-mining systems [16] to deal with both transactional and relational data. Unfortunately, the construction of universal relations will very likely lead to the introduction of redundant information, which will mislead the rule-discovery process of many data-mining algorithms. Existing data-mining algor

Existing data-mining algorithms (e.g., [1], [2], [18], [21], and [23]) can be made more powerful if they can overcome such a problem. They can also be further improved if they can discover rules that involve attributes that were not originally contained in a database. The FREE BANKING BOOKS PDF ability to do so is essential to the mining of interesting patterns in many different application areas. For example, rules regarding consumers’ buying habits at Christmas cannot be discovered if a new attribute of “holiday” has not been considered. Taking into consideration the need to address these issues, FARM II is equipped with some transformation functions that can be used to deal with both transactional and relational data and the different types of attributes FREE BANKING BOOKS DOWNLOAD PDF in the databases of a database system so as to construct new relations. To discover the interesting fuzzy association rules that are hidden in these transformed relations, FARM II makes use of an efficient rule-search process that is guided by an objective interestingness measure. This measure is defined in terms of fuzzy confidence and support measures that reflect the differences in the actual and expected degrees to which a tuple is characterized by different linguistic terms. {GOOGLEADS}The rest of this paper is organized as follows. FREE BANKING BOOKS In Section II, we provide a description of how the existing algorithms can be used for the mining of association rules and how fuzzy techniques can be applied to the data-mining process. In Section III,