Clustering, an unsupervised pattern classification method, plays an important role in
identifying input dataset structures. It partitions input datasets into clusters or groups where …

Hierarchical clustering techniques help in building a tree-like structure called dendrogram
from the data points which can be used to find the closest related data objects. This paper …

One of the major challenges in bioinformatics lies in identification of modified gene
expressions of an affected person due to medical ailments. Focused research has been …

In this work, a novel LAMDA (Learning Algorithm for Multivariable Data Analysis) control
strategy for trajectory tracking for an aerial manipulator is presented. Four control strategies …

Most of the time membership value in the fuzzy set cannot be exactly defined. Interval-
valued fuzzy set (IVFS) is a special type of type-2 fuzzy sets which represents the …

In this work, we propose a semi-supervised learning algorithm, which can solve problems of
classification, clustering, or a combination of them. This algorithm is based on the LAMDA …

The recently reported 'Improved Probabilistic Intuitionistic Fuzzy c-Means (IPIFCM)
algorithm'is a computationally efficient algorithm that does fuzzy clustering based on …

Aiming at weak point of classical validity index based on Euclidean distance, we proposed a
new validity index which based on Non-Euclidean Distance. First of all, we constructed …

Most of the time membership value in the fuzzy set cannot be exactly defined. Interval-
valued fuzzy set (IVFS) is a special type of type-2 fuzzy sets which represents the …

According to the current analysis of residents' electricity consumption behavior, with the
popularization of smart meters, to a certain extent, residents' electricity consumption data …