Data Mining INTRODUCTION: Put simply, data mining in computer Software is the drilling down for lost data that has lain dormant, sometimes for years. Often a company has not been aware it possessed this data-usually because of decentralized data base management, lack of relational data base systems, or the existence of legacy systems with old and forgotten databases. The real value of the data lies in analyzing it to reveal or create relationships that have been previously undiscovered. Having masses of data is of no value whatsoever if you don’t bother to evaluate it. Evaluation can relate to anything from sales records to seasonal correlations; it can be applied to any supplier –customer relationship, whether in the private or public sector or in industrial, commercial, or consumer markets. The results of data mining can be grouped as follows:
  • Association of events that can be correlated. A computer purchase, For example, is likely to involve the simultaneous purchase of a printer.
  • Sequences as one event leads to another. Computer and printer purchase may be followed by the purchase of a scanner.
  • Classification through the recognition of patterns. These can be based on any relevant data-income, turnover, location, or even average summer rainfall! It all depends on how you see the data benefiting your business.
  • Forecasting, this is a natural extrapolation from the other results and can facilitate more accurate predictions. Predicted beer consumption, for example, could also be related to future consumption of peanuts or crisps.
CONCLUSION: Letting your algorithms find the patterns in the data is what mining is about. When you use data mining; the best [target] you may choose is a good segmentation of your customer base as it relates to likelihood of churn. Because ,if you can figure out who’s about to churn, plus the chances of dissuading them, then you can make appropriately targeted offers to keep the customer with real revenue potential in the fold.
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