What are the procedures that we can implement in Transformation step?
Dear Deeman,
Balan has stated correctly. The steps in figure that you showed are very useful indeed.
Hi Deeman,
The general approaches of data transformation are: data normalization, discretization, aggregation, standardization, attribute construction, and concept hierarchy generation for nominal data.
HTH.
Dr. Samer Sarsam
@ Samer Sarsam
What are the difference between preprocessing and Transformation?
because the steps you mentioned I think included in preprocessing step
Thanks in advanc
Deeman
Data transformation is part of data preprocessing stage. It is a common approach that one can perform to convert the data from type to another in order to tackle certain data mining task. Hence, it would highly contribute toward the success of the mining process.
Cheers,
Dr. Samer Sarsam
KDD refers to the overall process of discovering useful knowledge from data. It involves the evaluation and possibly interpretation of the patterns to make the decision of what qualifies as knowledge. It also includes the choice of encoding schemes, preprocessing, sampling, and projections of the data prior to the data mining step. Data mining refers to the application of algorithms for extracting patterns from data without the additional steps of the KDD process.
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The overall process of finding and interpreting patterns from data involves the repeated application of the following steps:
1. Developing an understanding of
o the application domain
o the relevant prior knowledge
o the goals of the end-user
2. Creating a target data set: selecting a data set, or focusing on a subset of variables, or data samples, on which discovery is to be performed.
3. Data cleaning and preprocessing.
o Removal of noise or outliers.
o Collecting necessary information to model or account for noise.
o Strategies for handling missing data fields.
o Accounting for time sequence information and known changes.
4. Data reduction and projection.
o Finding useful features to represent the data depending on the goal of the task.
o Using dimensionality reduction or transformation methods to reduce the effective number of variables under consideration or to find invariant representations for the data.
5. Choosing the data mining task.
o Deciding whether the goal of the KDD process is classification, regression, clustering, etc.
6. Choosing the data mining algorithm(s).
o Selecting method(s) to be used for searching for patterns in the data.
o Deciding which models and parameters may be appropriate.
o Matching a particular data mining method with the overall criteria of the KDD process.
7. Data mining.
o Searching for patterns of interest in a particular representational form or a set of such representations as classification rules or trees, regression, clustering, and so forth.
8. Interpreting mined patterns.
9. Consolidating discovered knowledge.
Dear Deeman,
Balan has stated correctly. The steps in figure that you showed are very useful indeed.
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