Clustering Analysis And Its Application In Image Segmentation

Data Mining, also called as knowledge discovery of databases (KDD), is a processing procedure of extracting credible, novel, effective and understandable patterns from databases. Data Mining is a relatively young research and application area based on database techniques, which synthesizes multidisciplinary productions, such as logic statistics, machine learning, fuzzy theory and visual computing, in order to acquire usable information from database .It has achieved increasing attention in the past years,and has been applied to finance, insurance, communal facilities, government, education, telecommunication, software development of the bank, transporting, etc.Cluster analysis is an important technology in data mining. Clustering processes are always carried out in the condition with no pre-known knowledge, so the mostresearch task is to solve that how to get the clustering result in this premises. The most research about clustering is focused on clustering algorithms, the main purpose is to produce practical algorithms with better performance. Up to now, many clustering algorithms have been presented, but these algorithms are only suited special problems and users. Furthermore, they are imperfect both theoretically and methodologically,even severe fault. Optimizing deeply clustering algorithms will not only help to perfect its theory, but also its popularization and application.Clustering is one of the important tasks in the field of data mining.Fuzzy clustering analysis that introduces the theory of fuzzy sets,provides the capability that be used to deal with real data .And it has been widely used in many fields.In this thesis,we discussed typical fuzzy clustering algorithms.The adventages and disadvantages of these algorithms and the problems existing in these algorithms and the prospects of the fuzzy clustering algorithm are discussed.FCM clustering algorithm is one of the widely applied fuzzy algorithms at present.But FCM algorithm has some shortcomings. The FCM clustering algorithm is sensitive to the situation of initialization and easy to fall into the local minimum when iterating.In order to study FCM algorithms systematically and deeply, they are reviewed in this paper based on c-means algorithm,from metrics,entropy,and constraints on membership function or cluster centers.In order to overcome shortcoming of FCM algorithm,in this paper a improved fuzzy c-means clustering algorithm is put forward.The basic idea of the algorithm is modified subjection value by adding weighted value and the optimal choice for parameter of clusters c based on cluster validity function. To prove the availability of this improved FCM algorithm, we use the algorithm in image segmentation.Many researchers have done a lot of work and presented thousands of approaches on image segmentation. Unfortunately there is no universal method which could be used everywhere. There is even not an objective standard for evaluating segmenting algorithm. The clustering-based method is very important and wide-used in image segmentation. The most common method of clustering analysis for image segmentation is the FCM (Fuzzy c-means Clustering), which doesn't need setting any threshold, or get people involved. It is very significant for the automatization of image segmentation.In this paper,an approach incorporating spatial information is researched, which penalize the FCM objective function to constrain the behavior of the membership functions. From the point of view of neighboring membership constraint, a new clustering objective function is proposed, which results in FCM algorithm for image segmentation based on neighboring membership constraint. Experiments of the proposed algorithm on synthetic test image and realistic image prove its validity and better performance against noise.