Customer Sales Analysis System Based On Purchasing

Customer Sales Analysis System Based On Purchasing

Abstract:
In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as item set concise representations, redundancy reduction, and post processing. However, being generally based on statistical information, most of these methods do not guarantee that the extracted rules are interesting for the user. Thus, it is crucial to help the decision-maker with an efficient post processing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, we propose to use ontologies in order to improve the integration of user knowledge in the post processing task. Second, we propose the Rule Schema formalism extending the specification language proposed by Liu et al. for user expectations. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach over voluminous sets of rules, we were able, by integrating domain expert knowledge in the post processing step, to reduce the number of rules to several dozens or less. Moreover, the quality of the filtered rules was validated by the domain expert at various points in the interactive process.

Existing System:

Mining frequent closed item sets was proposed in order to reduce the number of frequent item sets. The CLOSET algorithm was proposed in as a new efficient method for mining closed item sets. The authors proposed the MAFIA algorithm based on depth-first traversal and several pruning methods as Parent Equivalence Pruning (PEP), FHUT, HUTMFI, or Dynamic Recording. The notion of subsumed rules, discussed in, describes a set of rules having the same consequent and several additional conditions in the antecedent regarding a certain rule. Proposed a new pruning measure (Minimum Improvement) described as the difference between the confidences of two rules in a specification/generalization relationship.
Disadvantages:

 The number of frequent closed item sets generated is reduced in comparison with the number of frequent item sets.
 The huge number of discovered rules makes very difficult for a decision maker to manually outline the interesting rules. Thus, it is crucial to help the decision maker with an efficient reduction of the number of rules.
 It is crucial to help the decision-maker with an efficient technique for reducing the number of rules.

Proposed System:

This paper proposes a new interactive post processing approach, ARIPSO (Association Rule Interactive post-Processing using Schemas and Ontology) to prune and filter discovered rules. First, we propose to use Domain Ontology in order to strengthen the integration of user knowledge in the post processing task. Second, we introduce Rule Schema formalism by extending the specification language proposed for user beliefs and expectations toward the use of ontology concepts. Furthermore, an interactive and iterative framework is designed to assist the user throughout the analyzing task. The interactivity of our approach relies on a set of rule mining operators defined over the Rule Schemas in order to describe the actions that the user can perform.
Advantages:
 The more the knowledge is represented in a flexible, expressive, and accurate formalism, the more the rule selection is efficient.
 It will be very useful for the user to be able to introduce in the GI language interesting additional information.
 The representation of user expectations is more general, and thus, filtered rules are more interesting for the user.

System Requirements:
Hardware Requirements:

Processor : Intel Duel Core.
Hard Disk : 60 GB.
Floppy Drive : 1.44 Mb.
Monitor : LCD Colour.
Mouse : Optical Mouse.
RAM : 512 Mb.

Software Requirements:
Operating system : Windows XP.
Coding Language : ASP.Net with C#
Data Base : SQL Server 2005

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