Hymenopteran Colony Stream Clustering Algorithm and Comparison with Particle Swarm Optimization and Genetic Optimization Clustering

2021 ◽  
Vol 18 (4) ◽  
pp. 1336-1341
Author(s):  
Nikhil Parafe ◽  
M. Venkatesan ◽  
Prabhavathy Panner
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Applied Mathematics ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Random Access ◽  
Genetic Optimization ◽  
Data Set ◽  
Swarm Optimization ◽  
Stream Clustering

Stream is endlessly inbound sequence of information, streamed information is unbounded and every information are often examined one time. Streamed information are often noisy and therefore the variety of clusters within the information and their applied mathematics properties will change over time, wherever random access to the information isn’t possible and storing all the arriving information is impractical. When applying data set processing techniques and specifically stream clustering Algorithms to real time information streams, limitation in execution time and memory have to be oblige to be thought-about carefully. The projected hymenopteran colony stream clustering Algorithmic is a clustering Algorithm which forms cluster according to density variation, in which clusters are separated by high density features from low density feature region with mounted movement of hymenopteran. Result shows that it created denser cluster than antecedently projected Algorithmic program. And with mounted movement of ants conjointly it decreases the loss of data points. And conjointly the changed radius formula of cluster is projected so as to increase performance of model to create it a lot of dynamic with continuous flow of information. And also we changed probability formula for pick up and drop to reduce oulier. Results from hymenopteran experiments conjointly showed that sorting is disbursed in 2 phases, a primary clustering episode followed by a spacing part. In this paper, we have also compared proposed Algorithm with particle swarm optimization and genetic optimization using DBSCAN and k -means clustering.

scholarly journals Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Mi-Yuan Shan ◽  
Ren-Long Zhang ◽  
Li-Hong Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Computational Study ◽  
Cloud Model ◽  
Probability Model ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Vague Sets ◽  
Benchmark Instances

We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO) to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO) in vague sets (IVSs) is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.

scholarly journals Parallel Computation Performingkernel-Based Clustering Algorithm Using Particle Swarm Optimization For The Big Data Analytics

2019 ◽  
Vol 8 (2) ◽  
pp. 4753-4756
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Data Analysis ◽  
Parallel Computation ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Similarity Measures ◽  
Swarm Optimization ◽  
Non Linear

Digital data has been accelerating day by day with a bulk of dimensions. Analysis of such an immense quantity of data popularly termed as big data, which requires tremendous data analysis scalable techniques. Clustering is an appropriate tool for data analysis to observe hidden similar groups inside the data. Clustering distinct datasets involve both Linear Separable and Non-Linear Separable clustering algorithms by defining and measuring their inter-point similarities as well as non-linear similarity measures. Problem Statement: Yet there are many productive clustering algorithms to cluster linearly; they do not maintain quality clusters.Kernel-based algorithms make use of non-linear similarity measures to define similarity while forming clusters specifically with arbitrary shapes and frequencies. Existing System:Current Kernel-based clustering algorithms have few restraints concerning complexity, memory, and performance. Time and Memory will increase equally when the size of the dataset increase. It is challenging to elect kernel similarity function for different datasets. We have classical random sampling and low-rank matrix approximation linear clustering algorithms with high cluster quality and low memory essentials. Proposed work: in our research, we have introduced a parallel computation performing Kernel-based clustering algorithm using Particle Swarm Optimization approach. This methodology can cluster large datasets having maximum dimensional values accurately and overcomes the issues of high dimensional datasets.

A Stochastic Disturbance of Particle Swarm Optimization for K-Means Clustering Method

2011 ◽  
Vol 268-270 ◽  
pp. 10-15
Author(s):  
Jun Yan Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Clustering Method ◽  
Swarm Optimization ◽  
Stochastic Disturbance ◽  
Searching Ability

This paper presents a hybrid-clustering algorithm that is a stochastic disturbance of particle swarm optimization (PSO) for K-means clustering method (SDPSO-K). The proposed algorithm can improve the particle global searching ability in PSO to avoid the K-means disadvantage of being easily trapped in a local optimal solution and to save the expensive computational cost of PSO clustering. The performance of the SDPSO-K, compared with three recently developed modified PSO techniques and related clustering algorithms for six datasets, indicates that the SDPSO-K algorithm is clearly and consistently superior in terms of precision and robustness.

Quantum-Behaved Particle Swarm Optimization Dynamic Clustering Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2757-2760 ◽  
Author(s):  
Chun Yan Zhang ◽  
Wei Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Learning Strategy ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Optimal Number ◽  
Data Sets ◽  
Dynamic Clustering ◽  
Swarm Optimization ◽  
Coding Method

This paper proposed a revised quantum-behaved particle swarm optimization algorithm utilizing comprehensive learning strategy to prevent the universal tendency of premature convergence, based on which introduced a novel data clustering algorithm as well. The optimal number of cluster could be automatically obtained by this novel clustering algorithm because a new special coding method for particles was used. Compared with another two dynamic clustering algorithms on five testing data sets, the proposed dynamic clustering algorithm based on the comprehensive learning strategy has the best performance and with the best potential application prospect.

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