132,203 research outputs found
Clustering of loose groups and galaxies from the Perseus--Pisces Survey
We investigate the clustering properties of loose groups in the
Perseus--Pisces redshift Survey (PPS). Previous analyses based on CfA and SSRS
surveys led to apparently contradictory results. We investigate the source of
such discrepancies, finding satisfactory explanations for them. Furthermore, we
find a definite signal of group clustering, whose amplitude exceeds the
amplitude of galaxy clustering (,
for the most significant case; distances are
measured in \hMpc). Groups are identified with the adaptive
Friends--Of--Friends (FOF) algorithms HG (Huchra \& Geller 1982) and NW
(Nolthenius \& White 1987), systematically varying all search parameters.
Correlation strenght is especially sensitive to the sky--link (increasing
for stricter normalization ), and to the (depth \mlim of the) galaxy
data. It is only moderately dependent on the galaxy luminosity function
, while it is almost insensitive to the redshift--link (both to
the normalization and to the scaling recipes HG or NW).Comment: 28 pages (LaTeX aasms4 style) + 5 Postscript figures ; ApJ submitted
on May 4th, 1996; group catalogs available upon request
([email protected]
Adaptive Unified Differential Evolution for Clustering
Various clustering methods to obtain optimal information continues to evolve one of its development is Evolutionary Algorithm (EA). Adaptive Unified Differential Evolution (AuDE), is the development of Differential Evolution (DE) which is one of the EA techniques. AuDE has self adaptive scale factor control parameters (F) and crossover-rate (Cr).. It also has a single mutation strategy that represents the most commonly used standard mutation strategies from previous studies.The AuDE clustering method was tested using 4 datasets. Silhouette Index and CS Measure is a fitness function used as a measure of the quality of clustering results. The quality of the AuDE clustering results is then compared against the quality of clustering results using the DE method.The results show that the AuDE mutation strategy can expand the cluster central search produced by ED so that better clustering quality can be obtained. The comparison of the quality of AuDE and DE using Silhoutte Index is 1:0.816, whereas the use of CS Measure shows a comparison of 0.565:1. The execution time required AuDE shows better but Number significant results, aimed at the comparison of Silhoutte Index usage of 0.99:1 , Whereas on the use of CS Measure obtained the comparison of 0.184:1
AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density
DBSCAN has been widely used in density-based clustering algorithms. However,
with the increasing demand for Multi-density clustering, previous traditional
DSBCAN can not have good clustering results on Multi-density datasets. In order
to address this problem, an adaptive Multi-density DBSCAN algorithm
(AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method
is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and
MinPts), which are the key parameters to determine the clustering results and
performance, therefore allowing the model to be applied to Multi-density
datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid
the complicated repetitive initialization operations. Furthermore, the variance
of the number of neighbors (VNN) is proposed to measure the difference in
density between each cluster. The experimental results show that our AMD-DBSCAN
reduces execution time by an average of 75% due to lower algorithm complexity
compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN
improves accuracy by 24.7% on average over the state-of-the-art design on
Multi-density datasets of extremely variable density, while having no
performance loss in Single-density scenarios. Our code and datasets are
available at https://github.com/AlexandreWANG915/AMD-DBSCAN.Comment: Accepted at DSAA202
Fuzzy adaptive resonance theory: Applications and extensions
Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully group data for preprocessing purposes, and improves results over the absence of quantization with statistical significance. --Abstract, page iv
A New Adaptive Elastic Net Method for Cluster Analysis
Clustering is inherently a highly challenging research problem. The elastic net algorithm is designed to solve the traveling salesman problem initially, now is verified to be an efficient tool for data clustering in n-dimensional space. In this paper, by introducing a nearest neighbor learning method and a local search preferred strategy, we proposed a new Self-Organizing NN approach, called the Adaptive Clustering Elastic Net (ACEN) to solve the cluster analysis problems. ACEN consists of the adaptive clustering elastic net phase and a local search preferred phase. The first phase is used to find a cyclic permutation of the points as to minimize the total distances of the adjacent points, and adopts the Euclidean distance as the criteria to assign each point. The local search preferred phase aims to minimize the total dissimilarity within each clusters. Simulations were made on a large number of homogeneous and nonhomogeneous artificial clusters in n dimensions and a set of publicly standard problems available from UCI. Simulation results show that compared with classical partitional clustering methods, ACEN can provide better clustering solutions and do more efficiently
Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations
The large number of possible configurations of modern software-based systems,
combined with the large number of possible environmental situations of such
systems, prohibits enumerating all adaptation options at design time and
necessitates planning at run time to dynamically identify an appropriate
configuration for a situation. While numerous planning techniques exist, they
typically assume a detailed state-based model of the system and that the
situations that warrant adaptations are known. Both of these assumptions can be
violated in complex, real-world systems. As a result, adaptation planning must
rely on simple models that capture what can be changed (input parameters) and
observed in the system and environment (output and context parameters). We
therefore propose planning as optimization: the use of optimization strategies
to discover optimal system configurations at runtime for each distinct
situation that is also dynamically identified at runtime. We apply our approach
to CrowdNav, an open-source traffic routing system with the characteristics of
a real-world system. We identify situations via clustering and conduct an
empirical study that compares Bayesian optimization and two types of
evolutionary optimization (NSGA-II and novelty search) in CrowdNav
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