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Computing and Machine Intelligence (ICMI), International Conference on

作者： Doreyed Muhammed Ahmed Al-Kerboly Dhamyaa Zohdi Fatah Al-Kerboly College of Education for Pure Science University Of Anbar Anbar Iraq Ministry of Education Al-Anbar Distinguished School Anbar Iraq

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This study compares the clustering algorithms used by four outstanding Iraqi institutions in Anbar, including Anbar University, Fallujah University, AI-Marf University and AI-Huda University. Meta-studies for clustering, studies by themselves ensure clustering, clustering Reinforcement learning, definition including clustering, and deep clustering include methods to be considered The analysis is based on three basic metrics of cluster performance: the Silhouette score, the Calinski-Harabasz index, and the Davies-Bouldin index. The collected evaluations show unique performance characteristics for each algorithm, which provides helpful information for algorithm selection in an educational setting. Meta-getting to know for clustering is an effective method. It works well on all parameters with a silhouette rating of 0.4655, a Calinski-Harabasz index of 189.8185, and a Davies-Bouldin index of 0.8202, just like 0.7287. The locating, which is supported by way of silhouette rankings and equal Calinski-Harabasz and Davies-Bouldin indexes, suggests that the efficacy of all 3 of those methods compares nicely inside the context of experimental measurements from the s. Deep clustering, alternatively, plays in another way, with an extensively decreased silhouette score of 0.0880 and extensively higher Calinski-Harabasz (83.4026) and Davies-Bouldin (3.2493) indices. This shows that its clustering performance may be more severe when compared to the other strategies studied. The most successful method is meta-gaining knowledge of for clustering, which plays nicely on all parameters with a silhouette rating of 0.4655, a Calinski-Harabasz index of 189.8185, and a Davis- Bouldin index of 0.8202, just like 0.7287 Finding this, similarly determined by silhouette scores and almost same Calinski-Harabasz, and Davis- Bouldin indices, shows that the effectiveness of these three technology is comparable in the context of experimental measures of the. On the other hand, deep clustering has un

关键词： Metalearning Measurement Reviews clustering algorithms Reinforcement learning Self-supervised learning Lead

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Exploiting GPUs to Accelerate clustering algorithms

Exploiting GPUs to Accelerate Clustering Algorithms

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IEEE/ACS International Conference of Computer Systems and Applications

作者： Mahmoud Al-Ayyoub Qussai Yaseen Moahmmed A. Shehab Yaser Jararweh Firas Albalas Elhadj Benkhelifa Jordan University of Science and Technology Mobile Fusion Applied Research Centre Staffordshire University

ISBN: (纸本)9781509043217

Big data is a main problem for data mining methods. Fortunately, the rapid advances in affordable high performance computing platforms such as the Graphics Processing Unit (GPU) have helped researchers in reducing the execution time of many algorithms including data mining algorithms. This paper discusses the utilization of the parallelism capabilities of the GPU to improve the performance of two common clustering algorithms, which are K-Means (KM) and Fuzzy C-Means (FCM) algorithms. Two main parallelism approaches are presented: pure and hybrid. These different versions are tested under different settings including two different GPU-equipped machines (a laptop and a server). The results show excellent improvement gains of the hybrid implementations compared with the pure parallel and sequential ones. On the laptop, the best gains of the hybrid implementations compared with the sequential ones are 113X for KM and 10.9X for FCM. As for the server, the best gains are 13.5X for KM and 16.3X for FCM. Moreover, the paper explores the usage of a recent memory management technique for GPU called Unified Memory (UM). The results show a decrease in the performance gain of the hybrid implementations that is equal to 44% for hybrid version of KM and 61% for FCM. On the other hand, the use of UM does introduce a small advantage for the pure parallel implementation.

关键词： Graphics processing units clustering algorithms Big Data Parallel processing Servers Linear programming Mathematical model

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New Assessment Criteria for clustering algorithms

New Assessment Criteria for Clustering Algorithms

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IEEE Workshop on Machine Learning for Signal Processing

作者： S.A. Salem A.K. Nandi Signal Processing and Communications Group Department of Electrical Engineering and Electronics University of Liverpool Liverpool UK

clustering algorithms offer several advantages over a manual grouping processes. Yet, the initial and random guess of most clustering algorithms, along with noise and outliers, affect the reliability of their results. In this paper, new clustering performance measures (CPM) for assessing the reliability of a clustering algorithm are proposed. In this paper, two parameters are used to define clustering performance measures - the first is the validation measure, which is used for determining how well the algorithm works at a given set of parameter values, and the second is a repeatability measure, which is used for studying the effect of initial conditions on the clusters membership. Furthermore, these CPMs can be used to evaluate clustering algorithms. Two different types of real-world data are used for such an evaluation procedure. The first is a communications signal data set representing one modulation scheme under noise condition, and the second is a breast cancer data set

关键词： clustering algorithms Partitioning algorithms Signal processing algorithms Iterative algorithms Breast cancer Data engineering Government Data analysis Quality assessment Stability

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Fuzzy clustering algorithms in Subjective Classification Tasks

Fuzzy Clustering Algorithms in Subjective Classification Tas...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： M.I. Chacon M. G. Ramirez DSP & Vision Laboratory at the Chihuahua Institute of Technology Mexico Mexico

This paper presents a case of study where two of the most used fuzzy clustering algorithms in pattern recognition tasks are analyzed under a classification problem that involves a high degree of subjectivity. The problem consists on the classification of seven types of wood defects called knots. The algorithms are the Abonyi-Szeifert modification of the Gath-Geva algorithm, GGAS, and the Gustafson-Keseel, GK. An improvement to the GK algorithm, GKM, is also proposed and analyzed. Besides the analysis of the algorithms, three different techniques are proposed to generate the design set of samples and the testing set of samples. Results of the study show that the GGAS and the GKM algorithms have a performance close to human performance.

关键词： Classification algorithms clustering algorithms Humans Algorithm design and analysis Feature extraction Gabor filters Testing Data analysis Biology computing Logic

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Comparison of Graph clustering algorithms for Recovering Software Architecture Module Views

Comparison of Graph Clustering Algorithms for Recovering Sof...

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(CSMR) European Conference on Software Maintenance and Reengineering

作者： Roberto Almeida Bittencourt Dalton Dario Serey Guerrero Formal Methods Group-Department of Systems and Computing UFCG Federal University of Campina Grande Campina Grande Paraiba Brazil UEFS-State University of Feira de Santana Feira de Santana Bahia Brazil

In the domain of software architecture recovery, classical clustering algorithms have been used to recover module views, while new ones have been proposed to tackle specific software architecture issues. Nonetheless, little information concerning their empirical evaluation in this context is presently available. This paper presents an empirical study that evaluates four clustering algorithms according to three previously proposed criteria: extremity of cluster distribution, authoritativeness, and stability, which were measured against consecutive releases of four different systems. Our results suggest that the k-means algorithm performs best in terms of authoritativeness and extremity and that the modularization quality algorithm produces more stable clusters. They also point out that fully automated clustering techniques alone cannot recover module views in a sensible way, but may provide a reasonable first step to speed up an expert-assisted architecture recovery process.

关键词： clustering algorithms Software algorithms Software architecture Partitioning algorithms Extremities Stability criteria Algorithm design and analysis Software maintenance Data mining Computer architecture

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A Multimodel Approach of Complex Systems Identification and Control Using Neural and Fuzzy clustering algorithms

A Multimodel Approach of Complex Systems Identification and ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： N. ElFelly J.-Y. Dieulot P. Borne M. Benrejeb LAGIS FRE CNRS 3303 Ecole Centrale de Lille Villeneuve d'Ascq France UR LARA Automatique ENIT Tunis Tunis Tunisia

This paper deals with a new approach for complex systems modeling and control based on neural and fuzzy clustering algorithms. It aims to derive a base of local models describing the system in the whole operating domain. The implementation of this approach requires three main steps: 1) determination of the structure of the model-base, the number of models are found out by using Rival Penalized Competitive Learning (RPCL), and the operating clusters are selected referring to the fuzzy K-means algorithm, 2) parametric model identification using the clustering results 3) determination of the global system control parameters obtained by a fusion of local control parameters. The case of a second order nonlinear system is studied to illustrate the efficiency of the proposed approach.

关键词： Computational modeling clustering algorithms Artificial neural networks Adaptation model Trajectory Algorithm design and analysis

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Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data

Knowledge extraction from maritime spatiotemporal data: An e...

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IEEE International Conference on Big Data

作者： Giannis Spiliopoulos Konstantinos Chatzikokolakis Dimitrios Zissis Evmorfia Biliri Dimitrios Papaspyros Giannis Tsapelas Spyros Mouzakitis MarineTraffic United Kingdom Department of Product and Systems Design Engineering University of the Aegean Syros Greece Decision and Support Systems Laboratory National and Technical University of Athens Athens Greece

In this paper we attempt to define the major trade routes which vessels of trade follow when travelling across the globe in a scalable, data-driven unsupervised way. For this, we exploit a large volume of historical AIS data, so as to estimate the location and connections of the major trade routes, with minimal reliance on other sources of information. We address the challenges posed due to the volume of data by leveraging distributed computing techniques and present a novel MapReduce based algorithmic approach, capable of handling skewed and nonuniform geospatial data. In the direction, we calculate and compare the performance (execution time and compression ratio) and accuracy of several mature clustering algorithms and present preliminary results.

关键词： clustering algorithms Artificial intelligence Ports (Computers) Trajectory Spatiotemporal phenomena Algorithm design and analysis Geospatial analysis

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An Investigation of Speaker clustering algorithms in Adverse Acoustic Environments

An Investigation of Speaker Clustering Algorithms in Adverse...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Meng-Zhen Li Xiao-Lei Zhang Center for Intelligent Acoustics and Immersive Communications Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728102436;9789881476852

Speaker clustering is an important problem of speech processing, such as speaker diarization, however, its behavior in adverse acoustic environments is lack of comprehensive study. To address this problem, we focus on investigating its components respectively. A speaker clustering system contains three components-a feature extraction front-end, a dimensionality reduction algorithm, and a clustering back-end. In this paper, we use the standard Gaussian mixture model based universal background model (GMM-UBM) as a front end to extract high-dimensional supervectors, and compare three dimensionality reduction algorithms as well as two clustering algorithms. The three dimensionality reduction algorithms are the principal component analysis (PCA), spectral clustering (SC), and multilayer bootstrap network (MBN). The two clustering algorithms are the k-means and agglomerative hierarchical clustering (AHC). We have conducted an extensive experiment with both in-domain and out-of-domain settings on the noisy versions of the NIST 2006 speaker recognition evaluation (SRE) and NIST 2008 SRE corpora. Experimental results in various noisy environments show that (i) the MBN based systems perform the best in most cases, while the SC based systems outperform the PCA based systems as well as the original supervector based systems; (ii) AHC is more robust than k-means.

关键词： clustering algorithms NIST Principal component analysis Feature extraction Dimensionality reduction Noise measurement Signal to noise ratio

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Sharp kernel clustering algorithms and their associated Grothendieck inequalities 10

Sharp kernel clustering algorithms and their associated Grot...

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Annual ACM-Society for Industrial and Applied Mathmatics Symposium on Discrete algorithms

作者： Subhash Khot Assaf Naor Research supported in part by NSF

ISBN: (纸本)9780898717013

In the kernel clustering problem we are given a (large) n × n symmetric positive semidefinite matrix A = (a_(ij)) with (sum from i=1 to n) (sum from j=1 to n) a_(ij) = 0 and a (small) k × k symmetric positive semidefinite matrix B = (b_(ij)). The goal is to find a partition {S_1,..., S_k} of {1,..., n} which maximizes (sum from i=1 to k) (sum from j=1 to k) (∑a_(pq), ((p,q)∈s_i×s_j) b_(ij). We design a polynomial time approximation algorithm that achieves an approximation ratio of R(B)~2/C(B), where R(B) and C(B) are geometric parameters that depend only on the matrix B, defined as follows: if b_(ij) = 〈v_i, v_j〉 is the Gram matrix representation of B for some v_1,...,v_k ∈ R~k then R(B) is the minimum radius of a Euclidean ball containing the points {v_1, ..., v_k}.

关键词： Matrix representation sums Positive semi-definite Rubidium clustering algorithms Boron Kernel Seeds Approximation algorithms inequalities geometric parameter matrices

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Fermat Distances: Metric Approximation, Spectral Convergence, and clustering algorithms

arXiv

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arXiv 2023年

作者： Trillos, Nicolás García Little, Anna McKenzie, Daniel Murphy, James M. Department of Statistics University of Wisconsin MadisonWI53706 United States Department of Mathematics Utah Center for Data Science University of Utah Salt Lake CityUT84112 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States Department of Mathematics Tufts University MedfordMA02155 United States

We analyze the convergence properties of Fermat distances, a family of density-driven metrics defined on Riemannian manifolds with an associated probability measure. Fermat distances may be defined either on discrete samples from the underlying measure, in which case they are random, or in the continuum setting, in which they are induced by geodesics under a density-distorted Riemannian metric. We prove that discrete, sample-based Fermat distances converge to their continuum analogues in small neighborhoods with a precise rate that depends on the intrinsic dimensionality of the data and the parameter governing the extent of density weighting in Fermat distances. This is done by leveraging novel geometric and statistical arguments in percolation theory that allow for non-uniform densities and curved domains. Our results are then used to prove that discrete graph Laplacians based on discrete, sample-driven Fermat distances converge to corresponding continuum operators. In particular, we show the discrete eigenvalues and eigenvectors converge to their continuum analogues at a dimension-dependent rate, which allows us to interpret the efficacy of discrete spectral clustering using Fermat distances in terms of the resulting continuum limit. The perspective afforded by our discrete-to-continuum Fermat distance analysis leads to new clustering algorithms for data and related insights into efficient computations associated to density-driven spectral clustering. Our theoretical analysis is supported with numerical simulations and experiments on synthetic and real image data. © 2023, CC BY.

关键词： clustering algorithms

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