Fuzzy Clustering-Based Filter
نویسندگان
چکیده
This paper introduces a filter, named FCF (Fuzzy Clusteringbased Filter), for removing redundant features, thus making it possible to improve the efficacy and the efficiency of data mining algorithms. FCF is based on the fuzzy partitioning of features into clusters. The number of clusters is automatically estimated from data. After the clustering process, FCF selects a subset of features from the obtained clusters. To do so, we study four different strategies that are based on the information provided by the fuzzy partition matrix. We also show that these strategies can be combined for better performance. Empirical results illustrate the performance of FCF, which in general has obtained competitive results in classification tasks when compared to a related filter that is based on the hard partitioning of features.
منابع مشابه
Clustering of Fuzzy Data Sets Based on Particle Swarm Optimization With Fuzzy Cluster Centers
In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...
متن کاملBilateral Weighted Fuzzy C-Means Clustering
Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...
متن کاملProposing a Novel Cost Sensitive Imbalanced Classification Method based on Hybrid of New Fuzzy Cost Assigning Approaches, Fuzzy Clustering and Evolutionary Algorithms
In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...
متن کاملImage Segmentation Method for Crop Nutrient Deficiency Based on Fuzzy C-Means Clustering Algorithm
As the fact that the emergence and development of crop nutrient deficiency has become more common nowadays, this research aims to find a method to segment and determine nutrient deficiency regions of crop images based on image processing technology. The experiment starts by obtaining 256 images of various crops such as oat, wheat, beet, maize, rye, potato, kidney been and sunflower with nutrien...
متن کاملA Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...
متن کامل