Mono-nuclear kernel function is presented in this paper based on the fuzzy c-means clustering algorithm for data clustering to do the improvement in the field of data mining, puts forward the fuzzy c-means clustering algorithm based on multiple kernel function (MKFCM) algorithm. Under fully unsupervised learning method, a set of Gaussian kernel function combination are assigned different weight...

a new powerful optimization algorithm inspired from colonizing weeds is utilized to solve the well-known quadratic assignment problem (qap) which is of application in a large number of practical areas such as plant layout, machinery layout and so on. a set of reference numerical problems from qaplib is taken in order to evaluate the efficiency of the algorithm compared with the previous ones wh...

The k-means algorithm is a widely used clustering technique. Here we will examine the performance of multiple implementations of the k-means algorithm in different settings. Our discussion will touch on the implementation of the algorithm in both python and C, and will also mention a 3rd party package for the k-means algorithm that is also written in C but provides python bindings. We will then...

A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...

participants and procedure The study was conducted using the paper-and-pencil method on 77 patients suffering from multiple sclerosis. The theory we applied was Hobfoll’s conservation of resources theory. We also analyzed the impact of personal and situational variables on the functioning of patients with different kinds of resources. results Cluster analysis was used to construct the profiles ...

The K-Means algorithm for clustering has the drawback of always maintaining K clusters. This leads to ineffective handling of noisy data and outliers. Noisy data is defined as having little similarity with the closest cluster’s centroid. In K-Means a noisy data item is placed in the most similar cluster, despite this similarity is low relative to the similarity of other data items in the same c...