Data Clustering using almost parameter free Differential Evolution technique
نویسندگان
چکیده
منابع مشابه
Data Clustering Using Multi-objective Differential Evolution Algorithms
The article considers the task of fuzzy clustering in a multi-objective optimization (MO) framework. It compares the relative performance of four recently developed multi-objective variants of Differential Evolution (DE) on over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorit...
متن کاملCTRNN Parameter Learning using Differential Evolution
Target behaviours can be achieved by finding suitable parameters for Continuous Time Recurrent Neural Networks (CTRNNs) used as agent control systems. Differential Evolution (DE) has been deployed to search parameter space of CTRNNs and overcome granularity, boundedness and blocking limitations. In this paper we provide initial support for DE in the context of two sample learning problems.
متن کاملKinetic Parameter Estimation Using Modified Differential Evolution
For the development of mathematical models in chemical engineering, the parameter estimation methods are very important as design, optimization and advanced control of chemical processes depend on values of model parameters obtained from experimental data. Nonlinearity in models makes the estimation of parameter more difficult and more challenging. This paper presents an evolutionary computatio...
متن کاملCustomer Data Clustering using Data Mining Technique
Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is very important in business process. Large companies are having huge volume of data but starving for knowledge. To overcome the organization current issue, the new breed of technique is required that has intelligence and capabilit...
متن کاملIntegrative Parameter-Free Clustering of Data with Mixed Type Attributes
Integrative mining of heterogeneous data is one of the major challenges for data mining in the next decade. We address the problem of integrative clustering of data with mixed type attributes. Most existing solutions suffer from one or both of the following drawbacks: Either they require input parameters which are difficult to estimate, or/and they do not adequately support mixed type attribute...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2010
ISSN: 0975-8887
DOI: 10.5120/1310-1811