Decision making and Feature reduction through Rough Sets
A new approach for feature selection, decision making for the areas of uncertainty
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The issues of Real World are Very large data sets, Mixed types of data, Uncertainty, Incompleteness, Data change, Use of background knowledge etc. Lot of knowledge related to the application can be enerated through these large data sets. Rough set is the methodology which can be used to deduce rules from these data sets. It offers mathematical tools to discover patterns hidden in data and hence used in the field of data mining. Rough Sets does not require any preliminary information as Fuzzy sets require membership values or probability is required in statistics. Hence this is its specialty. T...
The issues of Real World are Very large data sets, Mixed types of data, Uncertainty, Incompleteness, Data change, Use of background knowledge etc. Lot of knowledge related to the application can be enerated through these large data sets. Rough set is the methodology which can be used to deduce rules from these data sets. It offers mathematical tools to discover patterns hidden in data and hence used in the field of data mining. Rough Sets does not require any preliminary information as Fuzzy sets require membership values or probability is required in statistics. Hence this is its specialty. Two novel algorithms to find optimal Reducts of condition attributes based on the relative attribute dependency, out of which the first algorithms gives simple Reduct whereas the second one gives the Reduct with minimum attributes, and highlights on the case study of mushroom which consists of twenty two attributes depending on which the decision is taken whether the mushroom plant is edible or poisonous.
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