Distributed Fuzzy Clustering with Automatic Detection of the Number of Clusters
نویسندگان
چکیده
We present a consensus-based algorithm to distributed fuzzy clustering that allows automatic estimation of the number of clusters. Also, a variant of the parallel Fuzzy c-Means algorithm that is capable of estimating the number of clusters is introduced. This variant, named DFCM, is applied for clustering data distributed across different data sites. DFCM makes use of a new, distributed version of the Xie-Beni validity criterion. Illustrative experiments show that for sites having data from different populations the developed consensus-based algorithm can provide better results than DFCM.
منابع مشابه
Oil Reservoirs Classification Using Fuzzy Clustering (RESEARCH NOTE)
Enhanced Oil Recovery (EOR) is a well-known method to increase oil production from oil reservoirs. Applying EOR to a new reservoir is a costly and time consuming process. Incorporating available knowledge of oil reservoirs in the EOR process eliminates these costs and saves operational time and work. This work presents a universal method to apply EOR to reservoirs based on the available data by...
متن کاملBotOnus: an online unsupervised method for Botnet detection
Botnets are recognized as one of the most dangerous threats to the Internet infrastructure. They are used for malicious activities such as launching distributed denial of service attacks, sending spam, and leaking personal information. Existing botnet detection methods produce a number of good ideas, but they are far from complete yet, since most of them cannot detect botnets in an early stage ...
متن کاملImproving Imbalanced data classification accuracy by using Fuzzy Similarity Measure and subtractive clustering
Classification is an one of the important parts of data mining and knowledge discovery. In most cases, the data that is utilized to used to training the clusters is not well distributed. This inappropriate distribution occurs when one class has a large number of samples but while the number of other class samples is naturally inherently low. In general, the methods of solving this kind of prob...
متن کاملخوشهبندی خودکار داده‌ها با بهره‌گیری از الگوریتم رقابت استعماری بهبودیافته
Imperialist Competitive Algorithm (ICA) is considered as a prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA, at run time, the suggested method (ACICA) finds the optimum number of clusters while optim...
متن کاملA clustering approach for mineral potential mapping: A deposit-scale porphyry copper exploration targeting
This work describes a knowledge-guided clustering approach for mineral potential mapping (MPM), by which the optimum number of clusters is derived form a knowledge-driven methodology through a concentration-area (C-A) multifractal analysis. To implement the proposed approach, a case study at the North Narbaghi region in the Saveh, Markazi province of Iran, was investigated to discover porphyry ...
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 2011