Cluster Validation
How to validate the resulted clusters
Many clustering methods exist to be used in different situations according to the underlying data to be analysed and clustered.
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Many clustering methods exist to be used in different situations according to the underlying data to be analysed and clustered.
Hierarchical clustering is a method to group instances of a data set into a series of nested clusters or a tree of clusters called a dendrogram
Fuzzy sets are used in fuzzy logic and can be considered as a generalisation of set theory. An element can be a member of a particular set or not in set theory
Centroid-based or representative-based clustering is a method of finding the best k clusters of items in the D data set.
Unsupervised machine learning methods aim to find patterns or groups (clusters) in data sets so that the most similar items in the data set will be gathered in the same cluster
Multiple methods exist to measure the performance of a classification algorithm and classify a data set into two classes, positive and classified.
KNN classifier is a nonparametric lazy classifier. In nonparametric classification the algorithm does not assume any specific distribution for the data sets.
Support Vector Machine is a binary parametric classifier that classifies items by creating a hyperplane between classes.
A rule-based classifier uses a set of rules to classify items in a data set. The rules are formalised in the form of IF-THEN clause.
Decision Tree is a classification model which recursively partitions the data space into two parts. The split can be considered as a hyperplane parallel to one axis of the data space.