8,811 research outputs found
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis
The clustering ensemble technique aims to combine multiple clusterings into a
probably better and more robust clustering and has been receiving an increasing
attention in recent years. There are mainly two aspects of limitations in the
existing clustering ensemble approaches. Firstly, many approaches lack the
ability to weight the base clusterings without access to the original data and
can be affected significantly by the low-quality, or even ill clusterings.
Secondly, they generally focus on the instance level or cluster level in the
ensemble system and fail to integrate multi-granularity cues into a unified
model. To address these two limitations, this paper proposes to solve the
clustering ensemble problem via crowd agreement estimation and
multi-granularity link analysis. We present the normalized crowd agreement
index (NCAI) to evaluate the quality of base clusterings in an unsupervised
manner and thus weight the base clusterings in accordance with their clustering
validity. To explore the relationship between clusters, the source aware
connected triple (SACT) similarity is introduced with regard to their common
neighbors and the source reliability. Based on NCAI and multi-granularity
information collected among base clusterings, clusters, and data instances, we
further propose two novel consensus functions, termed weighted evidence
accumulation clustering (WEAC) and graph partitioning with multi-granularity
link analysis (GP-MGLA) respectively. The experiments are conducted on eight
real-world datasets. The experimental results demonstrate the effectiveness and
robustness of the proposed methods.Comment: The MATLAB source code of this work is available at:
https://www.researchgate.net/publication/28197031
LinkCluE: A MATLAB Package for Link-Based Cluster Ensembles
Cluster ensembles have emerged as a powerful meta-learning paradigm that provides improved accuracy and robustness by aggregating several input data clusterings. In particular, link-based similarity methods have recently been introduced with superior performance to the conventional co-association approach. This paper presents a MATLAB package, LinkCluE, that implements the link-based cluster ensemble framework. A variety of functional methods for evaluating clustering results, based on both internal and external criteria, are also provided. Additionally, the underlying algorithms together with the sample uses of the package with interesting real and synthetic datasets are demonstrated herein.
Compression and Classification Methods for Galaxy Spectra in Large Redshift Surveys
Methods for compression and classification of galaxy spectra, which are
useful for large galaxy redshift surveys (such as the SDSS, 2dF, 6dF and
VIRMOS), are reviewed. In particular, we describe and contrast three methods:
(i) Principal Component Analysis, (ii) Information Bottleneck, and (iii) Fisher
Matrix. We show applications to 2dF galaxy spectra and to mock semi-analytic
spectra, and we discuss how these methods can be used to study physical
processes of galaxy formation, clustering and galaxy biasing in the new large
redshift surveys.Comment: Review talk, proceedings of MPA/MPE/ESO Conference "Mining the Sky",
2000, Garching, Germany; 20 pages, 5 figure
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