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  • Writer's pictureDimit Chadha

Data Mining - Discovery Of Knowledge

Data mining is a technology that blends traditional data analysis methods with sophisticated algorithms for processing large volumes of data. It has also opened up exciting opportunities for exploring and analyzing new types of data and for analyzing old types of data in new ways.


Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets. Data Mining is all about discovering unsuspected/ previously unknown relationships amongst the data.


The insights derived via Data Mining can be used for marketing, fraud detection, and scientific discovery, etc. Data mining is also called Knowledge discovery, Knowledge extraction, data/pattern analysis, information harvesting, etc.


Data mining techniques can be used to support a wide range of business intelligence applications such as customer profiling, targeted marketing, work- flow management, store layout, and fraud detection.


Data mining answer's questions like -


"Who are the most profitable customers?"
"What products can be cross-sold or up-sold?"
"What is the revenue outlook of the company for next year?

Data mining is the process of automatically discovering useful information in large data repositories. Data mining techniques are deployed to scour large databases in order to find novel and useful patterns that might otherwise remain unknown. They also provide capabilities to predict the outcome of a future observation, such as predicting whether a newly arrived. customer will spend more than $100 at a department store


Important here is that not all information discovery tasks are data mining e.g. looking up contact phone number from telephone directory, any record from relational database systems


Concluding
  • Data Mining is all about explaining the past and predicting the future for analysis.

  • Data mining helps to extract information from huge sets of data. It is the procedure of mining knowledge from data.

  • Data mining process includes business understanding, Data Understanding, Data Preparation, Modelling, Evolution, Deployment.

  • Important Data mining techniques are Classification, clustering, Regression, Association rules, Outer detection, Sequential Patterns, and prediction

  • R-language and Oracle Data mining are prominent data mining tools.

  • Data mining technique helps companies to get knowledge-based information.

  • The main drawback of data mining is that many analytics software is difficult to operate and requires advance training to work on.

  • Data mining is used in diverse industries such as Communications, Insurance, Education, Manufacturing, Banking, Retail, Service providers, eCommerce, Supermarkets Bioinformatics.

Any Issues - Yes Offcourse

Data mining is not an easy task, as the algorithms used can get very complex and data is not always available at one place. It needs to be integrated from various heterogeneous data sources. These factors also create some issues.


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