Enhancing K means by unsupervised learning using PSO algorithm

Gupta, Aishwarya; Pattanaik, Vishwajeet; Singh, Mayank

doi:10.1109/ccaa.2017.8229805

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Let's first look at what particle swarm optimization is. Particle swarm optimization was first proposed by Eberhart and Kennedy and originated from the study of bird foraging behavior [22].…”

Section: A Model Overviewmentioning

confidence: 99%

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

Zhou

2019

IEEE Access

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With the explosive growth of information data in today's society, the continuous accumulation and increase of data in recent years make it difficult to extract useful information from it, so data mining comes into being. Association rule mining is an important part of data mining technology. Association rule mining is the discovery of frequent item sets in a large amount of data and the mining of strong association relations between them. Traditional association rule algorithms need to set minimum support and minimum confidence in advance. However, these two values are largely influenced by human subjectivity. Many scholars use average and weight to set these two values, but the effect is still not very good. In order to solve this problem, this paper proposed an improved algorithm of association rules-PSOFP growth algorithm, this algorithm is introduced into intelligent algorithm, particle swarm optimization algorithm, it can find the global optimal solution, we use this fact to find the optimal support, then using FP-growth algorithm for mining association rules, and finally put forward by information entropy to measure effectiveness in association rules mining, and the improved algorithm was applied to the social security event correlation analysis, the improved algorithm proved to our expectations.

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Section: A Model Overviewmentioning

confidence: 99%

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

Zhou

2019

IEEE Access

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“…Cluster can be called a group of similar objects, and clustering is a process of making similar sets out of raw data, which helps in segregation of unknown data easily. The parameters involved should be used cautiously as incompatible use of parameters of clustering like, Number of Clusters (k-means) and Density Limit, may lead to situations like improper density shape of clusters, ambiguity in finding centroid and the noise [5][6][7]. Mainly The improved semi supervised K mean clustering is used for the greedy iteration to find the K mean clustering is presented in [8].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Karthik¹,

Bhadoria²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.

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Enhancing knowledge discovery and management through intelligent computing methods: a decisive investigation

Ahamad,

Mishra

2024

Knowl Inf Syst

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Enhancing K means by unsupervised learning using PSO algorithm

Cited by 6 publications

References 10 publications

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

Particle Swarm Optimization-Based Association Rule Mining in Big Data Environment

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Enhancing knowledge discovery and management through intelligent computing methods: a decisive investigation

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