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In this study, a novel iterative optimization clustering algorithm is proposed by using a manifold distance based dissimilarity metric which can measure the geodesic distance along the manifold and a criterion function which can express the clustering target, that is the samples in the same cluster being somehow more similar than samples in different one. The steps of the algorithm are discussed in...
This paper presents the hierarchical cluster counting (HCC), a new quality metric for nondominated sets generated by multi-objective optimizers that is based on hierarchical clustering techniques. In the computation of the HCC, the samples in the estimate set are sequentially grouped into clusters. The nearest clusters in a given iteration are joined together until all the data is grouped in only...
Group-by is a core database operation that is used extensively in OLTP, OLAP, and decision support systems. In many application scenarios, it is required to group similar but not necessarily equal values. In this paper we propose a new SQL construct that supports similarity-based group-by (SGB). SGB is not a new clustering algorithm, but rather is a practical and fast similarity grouping query operator...
Noise clustering, as a robust clustering method, performs partitioning of data sets reducing errors caused by outliers. In many applications outliers contain important information and their correct identification are crucial. The original ant system algorithm is simplified leading to a generalized ant colony optimization algorithm that can be used to solve a wide variety of discrete optimization problems...
The task of continuous online unsupervised learning of streaming data in complex dynamic environments under conditions of uncertainty requires the maximizing (or minimizing) of a certain similarity-based objective function defining an optimal segmentation of the input data set into clusters, which is an NP-hard optimization problem in a general metric space and is computationally intractable for real-world...
We propose a self-adaptive hybrid evolutionary algorithm for the optimization of Morse clusters. The approach relies on a two-phase local optimization method to efficiently guide search. Individuals encode its own penalty settings and the algorithm evolves them simultaneously with the search for low energy clusters. Results show that the approach is efficient, as it is able to discover all optimal...
In this article we study the capacitated location routing problem (CLRP) which is defined as a combination of two problems: the facility location problem (FLP) and the vehicle routing problem (VRP). The CLRP is not just a purely academic construct; it has many applications in the practice. We propose a hybrid approach based on a tabu search algorithm combined with an improved ant colony system to...
Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent...
High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the others. In this paper, we propose a new clustering algorithm to solve these problems. The basic idea is to control the support of the optimization procedure so that the effect produced by those contaminated samples and dimensions is greatly...
Radio over Fiber (RoF) is technology where the RF signal is transmitted using optical carrier. There is one Central Station (CS) to which number of Base Stations (BS) is connected via optical fiber. Most of the signal processing is done at CS and BS contains only optical- electrical (O-E) and electrical- optical (E-O) converters and antenna for RF signal transmission. Now a large scale infrastructure...
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