A New Spatial Fuzzy C-Means for Spatial Clustering
نویسندگان
چکیده
Fuzzy C-means is a widely used clustering algorithm in data mining. Since traditional fuzzy C-means algorithms do not take spatial information into consideration, they often can’t effectively explore geographical data information. So in this paper, we design a Spatial Distance Weighted Fuzzy C-Means algorithm, named as SDWFCM, to deal with this problem. This algorithm can fully use spatial features to assign samples to different clusters, and it only needs to calculate the memberships one time, which reduces the running time greatly compared with other spatial fuzzy C-means algorithms. In addition, we also propose two new criteria, named as DESC and PESC, for evaluating spatial clustering results by measuring spatial and regular information separately. The experiments are carried out based on real petroleum geology data and artificial data, and the results show that SDWFCM can achieve better performance compared with traditional clustering method, and our spatial cluster indices can provide the assessment of clusters by taking spatial structure into consideration effectively. Key-Words: spatial clustering, fuzzy c-means, evaluation criteria.
منابع مشابه
Bilateral 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 کاملGeneralized Spatial Kernel based Fuzzy C-Means Clustering Algorithm for Image Segmentation
Image segmentation plays an important role in image analysis. It is one of the first and most important tasks in image analysis and computer vision. This proposed system presents a variation of fuzzy cmeans algorithm that provides image clustering. Based on the Mercer kernel, the kernel fuzzy c-means clustering algorithm (KFCM) is derived from the fuzzy c-means clustering algorithm (FCM).The KF...
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 کاملA multiobjective spatial fuzzy clustering algorithm for image segmentation
This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set...
متن کاملColor Video Segmentation Using Fuzzy C-mean Clustering with Spatial Information
Video segmentation can be considered as a clustering process that classifies one video succession into several objects. Spatial information enhances the quality of clustering which is not utilized in the conventional FCM. Normally fuzzy c-mean (FCM) algorithm is not used for color video segmentation and it is not robust against noise. In this paper, we presented a modified version of fuzzy c-me...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2015