Abstract:

The main objective of Utility Pattern Mining for Promo Code Generation project is to mine the frequent item set and maximum threshold signature of the shopping package software item set and to generate various promotion codes for the customers. The frequent item set deals with the whole database of the shopping application. It contains various transactions like sales data, purchase data, customer data, item data and etc. Here enhanced EHAUPM – Item set has been implemented (TKI) which gives more accuracy and performance than TKU (mining Top-K Utility item sets) and TKO (mining Top-K utility item sets in One phase), which are implemented in the existing methods for mining such item sets without the consideration of entire database. This may cause inaccurate results and improper output. These methods may use assumption purposes only.

EHAUPM -Item set method is used to analyze the items which are sold frequently that are reported by the client. The decisions can be made on the result of the analysis, so that the item can be identified. Normally an input given by the client to the sale the item in the project is taken as it is and service is provided without analyzing the input. This leads to wastage of time in decision making and also the delay in finding the frequently sold items. If the frequently sold items in the project are analyzed, then it is easy to find out the relationship or association among the items. So, that the reason for the sold items and how frequently sold item on each other can be found in a project.

A new concept called data engineering is used in the system to find associations or relationships among the frequently sold items. So that both data mining and networking concepts are implemented. Data mining refers to detecting of patterns and hidden information from the database. Several data mining techniques are available to mine the data and the results or the new or hidden information. The system provides information about the associations among the frequently sold item at an item level. The system has used two data mining techniques namely association rules and its algorithms to find the frequently sold items. Some of the results are displayed in a graphical manner also. The results of these techniques would be helpful in decision making so that the client’s needs can be satisfied in a faster way.

Through analyzing the entire database from the previous years, the data clarity will improve and make this system as a proven system. Here the key information is Item Level (IL), which denotes data density. A high utility item (HUI) is used as the input data set for the entire project. It contains a huge collection of utility items.

In Utility Pattern Mining for Promo Code Generation, Some Association Rules (AR) are implemented for frequent item mining. And finally TOP K – Item set has been implemented (TKI) for the core system.

Keywords:

Data Mining, Data Engineering, Utility Mining, Algorithm, Rule Mining,  Association Rules, Promo Code generation, Shopping software, purchase offer generation, sales offer, and promo code. MCA projects, MSc Projects, CSE Projects, IT Projects.

Helpline:

Visual Studio – 2012 , ASP.NET, C#, SQL Server, Java Script.

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