Neural network Self Organizing Map

Final year B.E proposal in "Dataware house based intelligent banking analysis system"
Additional heterogeneities

The data warehousing & data mining have changed the decision making process in modern day business environment, which basically equip the business companies to reach their customers with the right product and right offer at the right time. This project is mainly concentrated to analyze the customer churn behavior, fraud detection and customer relationship management (CRM) in a banking system. The project will be implemented with a completely warehouse based business intelligence tools with some of the data mining algorithms implemented during reporting phase for churn prediction and anomaly detection.Since customers usually churn from one company to another quite often and this too is happening at an alarming rate and is becoming the most important issue in customer relationship management, so customer retention is the need of the hour to ponder upon. Our project will implement different visualization methods & techniques through Oracle Business Intelligence tool to analyze churn behavior. For this we will implement classification & regression tree (CART) analysis. The pattern of fraud detection will be implemented as location and time-wise. Rule-based methods such as BAYES, FOIL or RIPPER or Support Vector Machines (SVM) or unsupervised neural network (NN)

Source: CODING EVERYTHING

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kohonen
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REVERSE MIRRORED SOM NUERAL NETWORK SCANNING #2
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Stock market analysts on trial

2002-02-09 14:24:25 by on-trial

The amount of poor and self-interested advice that is being issued by brokerages and their analysts. To this day, the majority of stockbrokers are compensated on the number of trades their customers make, not on the returns they generate for them or on the quality of the advice they provide. We believe that the price targets and analyst ratings are made with several masters in mind, none of whom are the individual investor. In a similar fashion, sell-side stock analysts are generally compensated based upon the overall profitability of their firms, not the quality or accuracy of their analysis. In the end, analysts have minimal structural incentive to be accurate in their predictions; rather their built-in incentive is to be as favorable to their corporate clients as possible. It is a...

Gurus' Results Stay Consistently Bad  — Forbes
Investment gurus make their money selling market predictions, not following them. Their overall performance has been historically and consistently dismal. Why people pay for market predictions is a one of Wall Street's biggest mysteries.

Brains.
Brains.

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