Efficient Hybridized Fuzzy Clustering with FCM-IQPSO for Biomedical Datasets

FCM: THE FUZZY c-MEANS CLUSTERING ALGORITHM

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering crit...

Fast SFFS-Based Algorithm for Feature Selection in Biomedical Datasets

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...

Combining PSO and FCM for Dynamic Fuzzy Clustering Problems

Optimized Clustering Techniques with Special Focus to Biomedical Datasets

The clinical data including clinical test results, MRI images and drug responses of patients are documented and analyzed with machine learning and data mining tools. The scale and complexity of these datasets is a big challenge to machine learning and data mining community as the data is of mixed type. The extraction of meaningful or desired information from these datasets provides knowledge in...

Efficient Clustering with Fuzzy Ants

In the past decade, various clustering algorithms based on the behaviour of real ants were proposed. The main advantage of these algorithms lies in the fact that no additional information, such as an initial partitioning of the data or the number of clusters, is needed. In this paper we show how the combination of the ant-based approach with fuzzy rules leads to an algorithm which is conceptual...