How to - Data Techniques for Optimal Results
Originally published: 26/01/2018 13:03
Publication number: ELQ-73482-1
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How to - Data Techniques for Optimal Results

Sunil Dutt Jha gives some tips on how to tweak your data mining exercises to obtain optimal results.


Faulty data mining makes seeking of decisive information akin to finding a needle in a haystack. Here are some tips to tweak your data mining exercises.

With enterprises operating out of multiple geographic locations, multi-database mining is becoming important for effective and informed decision making. The following data mining techniques will help you optimize your data mining efforts.

  • Step n°1 |

    Handling of incomplete data

    Incomplete data affects classification accuracy and hinders effective data mining. The following techniques are effective for working with incomplete data.

    1. The ISOM-DH model handles incomplete data using independent component analysis (ICA) and self-organizing maps (SOM). It uses existing data to estimate the missing data and visualize the handled high-dimensional data.

    2. Another data mining technique is based on the evolution of strategies built using parametric and non-parametric imputation methods. Genetic algorithms and multilayer perceptrons have to be applied to develop a framework to construct imputation strategies which address multiple incomplete attributes.

    3. Network approaches based on multi-task learning (MTL): the learning of a problem/instance in relation to others) for pattern classification, with missing inputs, can be compared with representative procedures used for handling incomplete data on two well-known data sets.

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  • Step n°2 |

    Ensure efficiency and scalability of data mining algorithms

    A great deal of expertise and effort is currently required for the implementation, maintenance, and performance-tuning of a parallel data mining application. These data mining techniques can help:

    1. Ensure parallel and scalable execution of data mining algorithms.
    2. Grid-enable data mining applications without any intervention on the application side.
    3. Opt for scalable data mining instead of mere associations when mining market basket data.
    4. Remove barriers to the widespread adoption of support vector machines.

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