FFramework for personal mobile commerce pattern mining ramework for personal mobile commerce pattern mining and prediction is a base paper for this project. and prediction is a base paper for this project.

By pattern mining and prediction we can govern the mobile By pattern mining and prediction we can govern the mobile user’s movements and transaction which he does.user’s movements and transaction which he does.

Mobile Commerce explores three major components.Mobile Commerce explores three major components. SSimilarity Inference Model(SIM)imilarity Inference Model(SIM) PPersonal Mobile Commerce Pattern (PMCP)ersonal Mobile Commerce Pattern (PMCP) MMobile commerce behavior predictorobile commerce behavior predictor

This is the current trend paper that facilitates mining and This is the current trend paper that facilitates mining and prediction of mobile user’s commerce behaviors in order to prediction of mobile user’s commerce behaviors in order to recommend stores and items previously unknown to the user.recommend stores and items previously unknown to the user.

Area of Work:Data mining:

It is a field at the intersection of computer science and statistics

It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

Types of Data mining: Anomaly Detection

Association rule learning

Clustering

Classification

Regression

Pattern mining:Pattern mining comes under Association rule

mining

Association rules came from the desire to analyze transaction data

Pattern mining plays a vital role in managing mobile commerce database.

We have mobile commerce technology to buy products online and transact money.

From that existing system we can easily predict the customer’s shopping behavior.

This system has offline mechanism for similarity inference and PMCPs Mining.

We have online engine for mobile commerce behavior.

We can easily predict the customer’s needs.

We uses this method for purchasing products as well as getting updates about products and for

secured transaction also.

We used Five types of algorithms for finding the customer behavior.

We can’t predict the user’s location using this system.

The system don’t know his situation today. It suggest goods as like yesterday .

In the new system we go for adding the locations also to predict the customer’s need.

For example, if we in thuckalai it will shows the common facilities available in thuckalai by finding our locations via network

Similarity Inference Model(SIM)

Personal Mobile Commerce Patterns(PMCP)

Mobile Commerce Behavior Predictor(MCBP)

Reference: Eric Hsueh-Chan Lu, Wang-Chien Lee, Member,

IEEE, and Vincent S. Tseng, Member, IEEE “A Framework for Personal Mobile Commerce Pattern Mining and Prediction”Vol 24,No.5,May 2012

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