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Computer Science and Electronic Engineering Conference (CEEC)

作者： Shabbar Naqvi Jonathan M. Garibaldi Intelligent Modelling and Analysis (IMA) Research Group School of Computer Science University of Nottingham UK

Fourier Transform Infrared Spectroscopy (FTIR) is a relatively new technique that has been frequently applied now a days in cancer pathology including breast cancer. The long term aim of this work is to develop novel techniques using machine learning methods for the analysis of FTIR data sets. This paper presents the preliminary work with a case study of a FTIR data set of breast cancer with two commonly used clustering algorithms of fuzzy c-means and k-means to differentiate between different cancer grades. We also discuss the complexities involved in the analysis of spectral data sets and need to find new methods. Future work will involve efforts towards development of a novel frame work with advanced machine learning methods to extract valuable information from complex spectral data sets.

关键词： clustering algorithms Breast cancer Algorithm design and analysis Spectroscopy Pathology Machine learning algorithms

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Evaluation of algebraic graph clustering algorithms for complex networks

Evaluation of algebraic graph clustering algorithms for comp...

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International Informatics and Software Engineering Conference (UBMYK)

作者： Kayhan Erciyes Maltepe University Istanbul Turkey

We review main graph clustering algorithms which are MST-based, Shared Nearest Neighbor and Edge-Betweenness algorithms and show novel algebraic graph implementations using Python. We compare them using randomly gener... 详细信息

ISBN: (纸本)9781665407601

关键词： Software algorithms clustering algorithms Complex networks Parallel processing Informatics Software engineering Python

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A cooperative game theoretic approach to clustering algorithms for wireless sensor networks

A cooperative game theoretic approach to clustering algorith...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Hui Jing Hitoshi Aida Department of Electrical Engineering and Information Systems Graduate School of Engineering University of Tokyo Japan

ISBN: (纸本)9781424445608

As one of the most widely investigated topology control mechanisms of wireless sensor networks (WSNs), the clustering algorithm provides energy efficient communications by reducing transmission overhead and enhancing transmission reliability. Through the previous forms of noncooperative games, the behavior of each sensor node (SN) is individual in WSNs; accordingly, it engenders uneven distribution of residual energy across SNs and expedites network partition. To balance energy consumption of SNs and increase network lifetime and stability, a cooperative game theoretic model of clustering algorithms is provided for assigning feasible allocations of energy cost. Moreover, from the outcome of this model, we propose and analyze a cooperative clustering approach for global optimization with the capacity of sensing data transmission and energy efficiency. The key idea is that SNs should trade off individual cost with network-wide cost. In the algorithm, we develop conditions to form coalitions considering residual energy, transmission distance and number of SNs in a cluster adapting to various WSNs. Furthermore, we present performance evaluation and comparison of the existing clustering algorithms with our approach quantitatively with respect to network lifetime, data transmission capacity and energy efficiency. Comparing with other approaches through the simulation, our scheme can surely guarantee to prolong network life-time and improve data transmission capacity up to 5.8% and 35.9%, respectively.

关键词： Game theory clustering algorithms Wireless sensor networks Energy efficiency Costs Data communication Partitioning algorithms Network topology Communication system control Telecommunication network reliability

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Data clustering algorithms based on Swarm Intelligence

Data clustering algorithms based on Swarm Intelligence

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International Conference on Electronic Computer Technology

作者： Pankaj K. Bharne V. S. Gulhane Shweta K. Yewale Sipna''s College of Engineering and Technology Amravati India PRMIT and R Badnera Amravati India

For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the procedure of most successful methods of optimization techniques inspired by Swarm Intelligence: Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This paper also gives a comparative analysis of PSO and ACO for data clustering.

关键词： Particle swarm optimization clustering algorithms Optimization Algorithm design and analysis Ant colony optimization Signal processing algorithms Genetic algorithms

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Big data and clustering algorithms

Big data and clustering algorithms

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International Conference on Research Advances in Integrated Navigation Systems (RAINS)

作者： V W Ajin Lekshmy D Kumar Department of Computer Science SCT College of Engineering Trivandrum India

ISBN: (纸本)9781509011124

Data mining is the method which is useful for extracting useful information and data is extorted, but the classical data mining approaches cannot be directly used for big data due to their absolute complexity. The data that is been formed by numerous scientific applications and incorporated environment has grown rapidly not only in size but also in variety in recent era. The data collected is of very large amount and there is difficulty in collecting and assessing big data. clustering algorithms have developed as a powerful meta learning tool which can precisely analyze the volume of data produced by modern applications. The main goal of clustering is to categorize data into clusters such that objects are grouped in the same cluster when they are “similar” according to similarities, traits and behavior. The most commonly used algorithm in clustering are partitioning, hierarchical, grid based, density based, and model based algorithms. A review of clustering and its different techniques in data mining is done considering the criteria's for big data. Where most commonly used and effective algorithms like K-Means, FCM, BIRCH, CLIQUE algorithms are studied and compared on big data perspective.

关键词： clustering algorithms Big data Algorithm design and analysis Partitioning algorithms Data mining Approximation algorithms Machine learning algorithms

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Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing

arXiv

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arXiv 2024年

作者： Aryal, Sunil Wells, Jonathan R. Baniya, Arbind Agrahari Santosh, K.C. Deakin University GeelongVIC Australia University of South Dakota VermillionSD United States

In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters. Our empirical results, obtained using three most widely used clustering algorithmsâĂŤnamely KMeans, DBSCAN, and DP (Density Peak)âĂŤacross a wide range of real-world datasets, show that clustering after ARES transformation produces better and more consistent results. Copyright © 2024, The Authors. All rights reserved.

关键词： clustering algorithms

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NISQ-friendly measurement-based quantum clustering algorithms

arXiv

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arXiv 2023年

作者： Patil, Srushti Banerjee, Shreya Panigrahi, Prasanta K. NNF Quantum Computing Programme Neils Bphr Institute University of Copenhagen Copenhagen NDK-2200 Denmark Department of Physics Indian Institute of Science Education and Research Andhra Pradesh Tirupati517619 India Center for Quantum Science and Technology Siksha'O' Anusandhan University Bhubaneswar Odisha 751030 India Institut Quantique Université de Sherbrooke QCJ1K2R1 Canada Department of Physical Sciences Indian Institute of Science Education and Research West Bengal Kolkata741246 India

Two novel measurement-based, quantum clustering algorithms are proposed basedon quantum parallelism and entanglement. The first algorithm follows a divisiveapproach. The second algorithm is based on unsharp measurements, where weconstruct an effect operator with a Gaussian probability distribution tocluster similar data points. A major advantage of both algorithms is that theyare simplistic in nature, easy to implement, and well suited for noisyintermediate scale quantum computers. We have successfully applied the firstalgorithm on a concentric circle data set, where the classical clusteringapproach fails, as well as on the Churrtiz data set of $130$ cities, where weshow that the algorithm succeeds with very low quantum resources. We appliedthe second algorithm on the labeled Wisconsin breast cancer dataset, and foundthat it is able to classify the dataset with high accuracy using only$O(log(D))$ qubits and polynomial measurements, where $D$ is the maximaldistance within any two points in the dataset. We also show that this algorithmworks better with an assumed measurement error in the quantum system, making itextremely well-suited for NISQ devices. © 2023, CC BY.

关键词： clustering algorithms

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Dynamic Energy-Efficient clustering algorithms Using Advanced Metaheuristics for Prolonged Network Lifetime in Wireless Sensor Networks

Dynamic Energy-Efficient Clustering Algorithms Using Advance...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： T. Sivakumar A. Sathiya M. Saravanan G. Uma S. Arivumani Samson A. Elangovan Dept. of Electronics & Communication Engg M. Kumarasamy College of Engineering (Autonomous) Dept. of Electronics & Communication Engg Full Time Research Scholar M. Kumarasamy College of Engineering (Autonomous) Dept. of Electronics & Communication Engg Faculty of Engineering & Technology Annamalai University Chidambaram Dept. of Mathematics Sathyabama Institute of Science and Technology Chennai Dept. of Electronics & Communication Engg Arunai Engineering College Tiruvannamalai

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Wireless Sensor Networks (WSNs) are pivotal in modern applications, yet challenges such as energy efficiency and network lifetime remain critical. This paper proposes a Dynamic Energy-Efficient clustering Algorithm (DEECA) utilizing hybrid metaheuristics combining Egret Swarm Optimization and Genetic algorithms. The proposed method dynamically selects cluster heads and optimizes clustering based on node energy, distance, and traffic conditions. Simulations were conducted on diverse WSN scenarios to evaluate energy consumption, network lifetime, packet delivery ratio, and throughput. Results demonstrate that DEECA outperforms traditional algorithms such as LEACH, HEED, and PSO by achieving a 20% increase in network lifetime and a 15% improvement in packet delivery ratio. Visual analyses include energy consumption trends, coverage heatmaps, and multi-metric radar plots. The findings validate DEECA's capability to enhance energy efficiency and scalability in WSNs, offering a robust framework for real-world deployments in resource-constrained environments.

关键词： Wireless communication Wireless sensor networks Energy consumption Visualization Heuristic algorithms Metaheuristics clustering algorithms Energy efficiency Particle swarm optimization Genetic algorithms

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Application of K-means clustering algorithms in Optimizing Logistics Distribution Routes

Application of K-means Clustering Algorithms in Optimizing L...

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International Conference on Systems and Informatics (ICSAI)

作者： Ma Haonan Yong Chen He Meng Huang Yana Wen Yuhan Cheng Yifei Jin University of Institute of Disaster Prevention SanHe HeBei China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

With the development of information technology and the arrival of the era of big data, China's logistics industry has been developing rapidly, especially in modern logistics management information system, In order to make full use of all kinds of resources and reduce logistics cost, this project extensively applies various information technologies related to mechanical learning. Taking the logistics distribution system of YifengWeiye Group as the research object, this paper puts forward the theory of K-means clustering algorithm and mileage-saving algorithm, adopts the idea of spatial clustering analysis regionalization, applies Python technology to process the special attribute data of transportation contained in the process of logistics distribution, and applies entity recognition technology based on attribute value partitioning algorithm and K-means clustering algorithm to realize the data area.

关键词： clustering algorithms Logistics Partitioning algorithms Classification algorithms Optimization Market research

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Improving the open cluster census. I. Comparison of clustering algorithms applied to Gaia DR2 data

arXiv

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arXiv 2020年

作者： Hunt, Emily L. Reffert, Sabine Landessternwarte Zentrum für Astronomie der Universität Heidelberg Königstuhl 12 Heidelberg69117 Germany

Context. The census of open clusters in the Milky Way is in a never-before seen state of flux. Recent works have reported hundreds of new open clusters thanks to the incredible astrometric quality of the Gaia satellite, but other works have also reported that many open clusters discovered in the pre Gaia era may be associations. Aims. We aim to conduct a comparison of clustering algorithms used to detect open clusters, attempting to statistically quantify their strengths and weaknesses by deriving the sensitivity, specificity, and precision of each as well as their true positive rate against a larger sample. Methods. We selected DBSCAN, HDBSCAN, and Gaussian mixture models for further study, owing to their speed and appropriateness for use with Gaia data. We developed a preprocessing pipeline for Gaia data and developed the algorithms further for the specific application to open clusters. We derived detection rates for all 1385 open clusters in the fields in our study as well as more detailed performance statistics for 100 of these open clusters. Results. DBSCAN was sensitive to 50% to 62% of the true positive open clusters in our sample, with generally very good specificity and precision. HDBSCAN traded precision for a higher sensitivity of up to 82%, especially across different distances and scales of open clusters. Gaussian mixture models were slow and only sensitive to 33% of open clusters in our sample, which tended to be larger objects. Additionally, we report on 41 new open cluster candidates detected by HDBSCAN, three of which are closer than 500 pc. Conclusions. When used with additional post-processing to mitigate its false positives, we have found that HDBSCAN is the most sensitive and effective algorithm for recovering open clusters in Gaia data. Our results suggest that many more new and already reported open clusters have yet to be detected in Gaia data. © 2020, CC BY.

关键词： clustering algorithms

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