UNSUPERVISED DATA AND HISTOGRAM CLUSTERING USING INCLINED PLANES SYSTEM OPTIMIZATION ALGORITHM
نویسندگان
چکیده
منابع مشابه
IPO: An Inclined Planes System Optimization Algorithm
In the last decades, heuristic algorithms are widely used in solving problems in different fields of science and engineering. Most of these methods are inspired by natural phenomena, such as biological behaviours or physical principles.
متن کاملMulti Objective Inclined Planes System Optimization Algorithm for VLSI Circuit Partitioning
In this paper multi objective optimization problem for partitioning process of VLSI circuit optimization is solved using IPO algorithm. The methodology used in this paper is based upon the dynamic of sliding motion along a frictionless inclined plane. In this work, modules and elements of the circuit are divided into two smaller parts (components) in order to minimize the cutsize and area imbal...
متن کاملHistogram Clustering for Unsupervised
This paper introduces a novel statistical mixture model for probabilistic grouping of distributional (histogram) data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an e cient multiscale formulation is developed. We present a prototypical application of thi...
متن کاملclustering iran earthquake data using improved ant system-based clustering algorithm (technical note)
clustering technique is one of the most important techniques of data mining and is the branch of multivariate statistical analysis and a method for grouping similar data in to same clusters. with the databases getting bigger, the researchers try to find efficient and effective clustering methods so that they can make fast and real decisions. thus, in this paper, we proposed an improved ant syst...
متن کاملUnsupervised clustering algorithm for N-dimensional data.
Cluster analysis is an important tool for classifying data. Established techniques include k-means and k-median cluster analysis. However, these methods require the user to provide a priori estimations of the number of clusters and their approximate location in the parameter space. Often these estimations can be made based on some prior understanding about the nature of the data. Alternatively,...
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: Image Analysis & Stereology
سال: 2014
ISSN: 1854-5165,1580-3139
DOI: 10.5566/ias.v33.p65-74