Question

Fuzzy logic modeling has many advantages over the conventional rule induction algorithm. For the discussion forum, you work in the admissions office of a University. There are a large number of applicants to the University. You classify them into three clusters-admitted, rejected, and those who should be admitted. For the third cluster, how would you handle this taking into consideration the fuzzy logic modeling? Would ranking be a consideration?

need 300 words with no plagrism

Answer 1

Fuzzy logic modeling has many advantages over the conventional rule induction algorithm, such as:

• Fuzzy algorithms are robust.
• Since it's reasoning process is quite simple, it's computation power is saved.
• Fuzzy methods have a shorter development time than conventional methods.

Fuzzy logic is an ordered and mathematical method of manipulating essentially imprecise concepts through the use of membership functions, which allows members with a certain degree.

Fuzzy logic is a superset of conventional logic that has been continued to manipulate the concept of partial truth-truth values between ‘absolutely true’ and ‘absolutely false’. It is the logic underlying modes of reasoning which are approximate.

Cluster analysis classifies data into clusters or groups in the way that similar data objects belong to the same cluster and different data objects belong to different clusters.

Partition clustering algorithms classify the data sets into clusters or classes whereas different data objects should belong to different clusters. In hierarchical clustering, objects that belong to a child cluster also belong to the parent cluster.

The primary theories that apply in fuzzy modeling are the fuzzy set theory and fuzzy logic. Fuzzy modeling facilitates the transformation of a linguistic description to an algorithm with practical results. The variables in the given admission data represent the fuzzy subsets of the entire data set.

In this case, the ranking will not be an important consideration. The K-means algorithm will be used to group data but the problem will be finding the optimal centers of clusters and so fuzzy logic will be introduced to establish the optimal centers of the clusters. By clustering, it will be easier to identify the set that the object. Since there is no prior knowledge of elements’ clusters, similarities will be evaluated based on the attribute values that best describe the data. The fuzzy logic will then form the variables that emerge from the data relationship.