BUSINESS INTELLIGENCE DATA MINING TECHNIQUES
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Data Mining Techniques
Neural Networks/Pattern Recognition - Neural Networks are used in a blackbox fashion. One
creates a test data set, lets the neural network learn patterns based on known outcomes, then sets the neural
network loose on huge amounts of data. For example, a credit card company has 3,000 records, 100 of which
are known fraud records. The data set updates the neural network to make sure it knows the difference
between the fraud records and the legitimate ones. The network learns the patterns of the fraud records.
Then the network is run against company’s million record data set and the network spits out the records with
patterns the same or similar to the fraud records. Neural networks are known for not being very helpful in
teaching analysts about the data, just finding patterns that match. Neural networks have been used for
optical character recognition to help the Post Office automate the delivery process without having to use
humans to read addresses.
Memory Based Reasoning - This technique has results similar to neural network but goes about it
differently. MBR looks for "neighbor" kind of data, rather than patterns. If you look at insurance claims and
want to know which the adjudicators should look at and which they can just let go through the system, you
would set up a set of claims you want adjudicated and let the technique find similar claims.
Cluster Detection/Market Basket Analysis - This is where the classic beer/diapers bought
together analysis came from. It finds groupings. Basically, this technique finds relationships in product or
customer or wherever you want to find associations in data.
Link Analysis - This is another technique for associating like records. Not used too much, but there are
some tools created just for this. As the name suggests, the technique tries to find links, either in cu