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Multi Relational Data Mining Approaches in the Panchayat Raj Department in Orissa Govt

Dr. Pragnayaban Mishra, Neelamadhab Padhy, Rasmita Panigrahi


This paper presents the method of mining data in a multiple –relational data base that contains some information about the large data sets of Panchayat Raj Department (PR) Departments of Orissa (Store, Product, Shoppers in the different blocks .Data mining algorithms look for patterns in data. While most existing data mining approaches look for patterns in a single data table but multi-relational data mining (MRDM) approaches look for patterns that involve multiple tables (relations) from a relational database. Accurate demand forecasting remains difficult and challenging in today’s competitive and dynamic business environment, but even a little improvement in demand prediction may result in significant saving for retailers and manufactures. Multi-relational data mining can analyze data from multiple relations directly without the need to transfer the data into a single relation first. Two data mining models are proposed in this paper, which are Pure Classification (PC) model and Hybrid Clustering classification (HCC) model. Pure Classification model uses k-Nearest Neighbor Classification technique, and Hybrid Clustering Classification first uses k-Mean Mode Clustering to define clusters and then uses k-Nearest Neighbor classification to find k most similar objects. Hybrid Clustering Classification model introduces a concept of combining existing data mining techniques on the multi-relational data sets.


Multi Relational Data Mining, Classification, Clustering, Demand Forecasting.

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