Fuzzy & Data Minig September 20, 2007
Posted by nhabibi in Artificial Intelligence, Data Mining, Fuzzy, Soft Computing.1 comment so far
Fuzzy sets are sets whose elements have degrees of membership. Fuzzy sets have been introduced by Professor Lotfali Askarzadeh (1965) as an extension of the classical notion of set.
In classical set theory, an element either belongs or does not belong to the set. By contrast, fuzzy set theory an element belongs to the set, with membership degree in interval [0-1].
An example:
Assume that we have four students with bellow scores in biology exam:
Richard, 20
frank, 18
Anna, 14
Juli, 11
We want to define “A = set of Good Student in Biology Class“:
1- in classical set theory: A = {Richard, Frank}
2- in fuzzy set theory: A = { (Richard,1) , (Mark,0.7) , (Anna,0.3) , (Juli,0.1) }
In fact, we have associated a membership degree to each member, according to goodness of their score.
Fuzzy logic is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic.
Fuzzy concept has application in many areas, specially in control systems.
In many topics, after proposing an algorithm, the fuzzy version of the algorithm is proposed and evaluated against the original one.
Here, is our report, in Persian, that introduces data mining in general, Apriori algorithm to extract association rules, a fuzzy-based Apriori algorithm, Decision Tree and fuzzy-based Decision Tree.
More information:
- Zadeh’s 1965 paper on Fuzzy Sets (It has more that 5000 citations till now!)
