A Method for Dynamic Clustering of Data
نویسندگان
چکیده
This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Kohonen maps, elastic nets and fuzzy c-means). The work is based on an unified framework for constrained clustering recently proposed by the authors in [1]. This framework is extended by using a motion model for the clusters which includes global and local evolution of the data centroids. A noise model is also proposed to increase the robustness of the dynamic clustering algorithm with respect to outliers.
منابع مشابه
Clustering and Memory-based Parent-Child Swarm Meta-heuristic Algorithm for Dynamic Optimization
So far, various optimization methods have been proposed, and swarm intelligence algorithms have gathered a lot of attention by academia. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clus...
متن کاملA Hybrid Time Series Clustering Method Based on Fuzzy C-Means Algorithm: An Agreement Based Clustering Approach
In recent years, the advancement of information gathering technologies such as GPS and GSM networks have led to huge complex datasets such as time series and trajectories. As a result it is essential to use appropriate methods to analyze the produced large raw datasets. Extracting useful information from large data sets has always been one of the most important challenges in different sciences,...
متن کاملMulti-layer Clustering Topology Design in Densely Deployed Wireless Sensor Network using Evolutionary Algorithms
Due to the resource constraint and dynamic parameters, reducing energy consumption became the most important issues of wireless sensor networks topology design. All proposed hierarchy methods cluster a WSN in different cluster layers in one step of evolutionary algorithm usage with complicated parameters which may lead to reducing efficiency and performance. In fact, in WSNs topology, increasin...
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 کاملIdentification of Power Stripping Resources with Fuzzy Cluster Dynamic Approach (Case Study: West Azerbaijan Province)
Reducing electric power theft is a significant part of the potential benefits of implementing the concept of smart grid. This paper proposes a data-based approach to identify locations with unusual electricity consumption. The new distance-based method classifies the new data as violator costumers, if their distance is long to the primary consumption data. The proposed algorithm determines the ...
متن کاملFUZZY K-NEAREST NEIGHBOR METHOD TO CLASSIFY DATA IN A CLOSED AREA
Clustering of objects is an important area of research and application in variety of fields. In this paper we present a good technique for data clustering and application of this Technique for data clustering in a closed area. We compare this method with K-nearest neighbor and K-means.
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 1998