Data Mining Applied to Oil Well Using K-Means and DBSCAN

Lu, Chang; Shi, Yueting; Chen, Yueyang; Bao, Shiqi; Tang, Lei

doi:10.1109/ccbd.2016.018

Cited by 5 publications

(2 citation statements)

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“…Unlike partitioning algorithms, DBSCAN does not require any specification regarding the number of clusters. Only two parameters need to be taken into account: the maximum radius of each cluster " " and the smallest number of cluster points "M inP ts" which represents the density threshold of a dense region [25]. The process of the DBSCAN algorithm is as follows.…”

Section: A Clustering Algorithmsmentioning

confidence: 99%

Dynamic reallocation of SLA parameters in passive optical network based on clustering analysis

Frigui

Lemlouma

Gosselin

et al. 2018

2018 21st Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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Section: A Clustering Algorithmsmentioning

confidence: 99%

Dynamic reallocation of SLA parameters in passive optical network based on clustering analysis

Frigui

Lemlouma

Gosselin

et al. 2018

2018 21st Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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“…Clustering group large data into segments or clusters. The well-known clustering algorithms are the k-means, kmedoids, c-means, hierarchical and DBSCAN [35][36][37][38]. But these algorithms suffer from scalability problems and need improvements in it for the massive data.…”

Section: A Data Setsmentioning

confidence: 99%

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Khandare¹,

Alvi²

2018

IJISA

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Analyzing data is a challenging task nowadays because the size of data affects results of the analysis. This is because every application can generate data of massive amount. Clustering techniques are key techniques to analyze the massive amount of data. It is a simple way to group similar type data in clusters. The key examples of clustering algorithms are k-means, kmedoids, c-means, hierarchical and DBSCAN. The kmeans and DBSCAN are the scalable algorithms but again it needs to be improved because massive data hampers the performance with respect to cluster quality and efficiency of these algorithms. For these algorithms, user intervention is needed to provide appropriate parameters as an input. For these reasons, this paper presents modified and efficient clustering algorithm. This enhances cluster's quality and makes clusters more cohesive using domain knowledge, spectral analysis, and split-merge-refine techniques. Also, this algorithm takes care to minimizing empty clusters. So far no algorithm has integrated these all requirements that proposed algorithm does just as a single algorithm. It also automatically predicts the value of k and initial centroids to have minimum user intervention with the algorithm. The performance of this algorithm is compared with standard clustering algorithms on various small to large data sets. The comparison is with respect to a number of records and dimensions of data sets using clustering accuracy, running time, and various clusters validly measures. From the obtained results, it is proved that performance of proposed algorithm is increased with respect to efficiency and quality than the existing algorithms.

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Spatiotemporal Data-Adaptive Clustering Algorithm: An Intelligent Computational Technique for City Big Data

You

2021

Annals of the American Association of Geographers

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Data Mining Applied to Oil Well Using K-Means and DBSCAN

Cited by 5 publications

References 0 publications

Dynamic reallocation of SLA parameters in passive optical network based on clustering analysis

Dynamic reallocation of SLA parameters in passive optical network based on clustering analysis

Efficient Clustering Algorithm with Enhanced Cohesive Quality Clusters

Spatiotemporal Data-Adaptive Clustering Algorithm: An Intelligent Computational Technique for City Big Data

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