18,278 research outputs found
Clustering large-scale data based on modified affinity propagation algorithm
Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering
Clustering by soft-constraint affinity propagation: Applications to gene-expression data
Motivation: Similarity-measure based clustering is a crucial problem
appearing throughout scientific data analysis. Recently, a powerful new
algorithm called Affinity Propagation (AP) based on message-passing techniques
was proposed by Frey and Dueck \cite{Frey07}. In AP, each cluster is identified
by a common exemplar all other data points of the same cluster refer to, and
exemplars have to refer to themselves. Albeit its proved power, AP in its
present form suffers from a number of drawbacks. The hard constraint of having
exactly one exemplar per cluster restricts AP to classes of regularly shaped
clusters, and leads to suboptimal performance, {\it e.g.}, in analyzing gene
expression data. Results: This limitation can be overcome by relaxing the AP
hard constraints. A new parameter controls the importance of the constraints
compared to the aim of maximizing the overall similarity, and allows to
interpolate between the simple case where each data point selects its closest
neighbor as an exemplar and the original AP. The resulting soft-constraint
affinity propagation (SCAP) becomes more informative, accurate and leads to
more stable clustering. Even though a new {\it a priori} free-parameter is
introduced, the overall dependence of the algorithm on external tuning is
reduced, as robustness is increased and an optimal strategy for parameter
selection emerges more naturally. SCAP is tested on biological benchmark data,
including in particular microarray data related to various cancer types. We
show that the algorithm efficiently unveils the hierarchical cluster structure
present in the data sets. Further on, it allows to extract sparse gene
expression signatures for each cluster.Comment: 11 pages, supplementary material:
http://isiosf.isi.it/~weigt/scap_supplement.pd
Scaling Analysis of Affinity Propagation
We analyze and exploit some scaling properties of the Affinity Propagation
(AP) clustering algorithm proposed by Frey and Dueck (2007). First we observe
that a divide and conquer strategy, used on a large data set hierarchically
reduces the complexity to , for a
data-set of size and a depth of the hierarchical strategy. For a
data-set embedded in a -dimensional space, we show that this is obtained
without notably damaging the precision except in dimension . In fact, for
larger than 2 the relative loss in precision scales like
. Finally, under some conditions we observe that there is a
value of the penalty coefficient, a free parameter used to fix the number
of clusters, which separates a fragmentation phase (for ) from a
coalescent one (for ) of the underlying hidden cluster structure. At
this precise point holds a self-similarity property which can be exploited by
the hierarchical strategy to actually locate its position. From this
observation, a strategy based on \AP can be defined to find out how many
clusters are present in a given dataset.Comment: 28 pages, 14 figures, Inria research repor
K
The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI) for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
SIMCO: SIMilarity-based object COunting
We present SIMCO, the first agnostic multi-class object counting approach.
SIMCO starts by detecting foreground objects through a novel Mask RCNN-based
architecture trained beforehand (just once) on a brand-new synthetic 2D shape
dataset, InShape; the idea is to highlight every object resembling a primitive
2D shape (circle, square, rectangle, etc.). Each object detected is described
by a low-dimensional embedding, obtained from a novel similarity-based head
branch; this latter implements a triplet loss, encouraging similar objects
(same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this
embedding for clustering, so that different types of objects can emerge and be
counted, making SIMCO the very first multi-class unsupervised counter.
Experiments show that SIMCO provides state-of-the-art scores on counting
benchmarks and that it can also help in many challenging image understanding
tasks
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