User clustering based on Canopy+K-means algorithm in cloud computing

Tong, Jianfei

doi:10.1080/09720502.2017.1386476

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Thus, centroids are calculated for these subsets. This technique speeds up the processing of large datasets and shows great efficiency and efficacy (Tong, 2017; Xia, Ning, & He, 2020; Zhang, Zhang, & Zhang, 2018).…”

Section: Clustering Techniques Reviewmentioning

confidence: 99%

A framework for spatial regionalization composed of novel clustering‐based algorithms under spatial contiguity constraints

Miranda

Viterbo

Bernardini

2022

Transactions in GIS

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Traditionally, the geospatial regionalization task consists of aggregating into regions, geographically connected areas that share similar characteristics. Although various spatial optimization approaches have been proposed for finding exact regionalization solutions, these approaches are not practical when applied to a large number of areas or problems for online aggregation, due to the long execution times using hardware with low resources. In this article, we present a framework for executing spatial regionalization tasks. The precondition for using our framework is the definition of a map describing the neighborhood relations-as spatial contiguity constraints-among the areas (or objects) to be regionalized. In our framework we implemented three clustering algorithms with spatial contiguity constraints: RegK-Means and Agglomerative Hierarchical Regionalization (AHR), our adaptation of k-means partition-based clustering and hierarchicalbased clustering algorithms, respectively; and the Automatic Zoning Procedure (AZP), a traditional algorithm for regionalization that also has the premise of simplifying the neighborhood relation representation. We conducted an exploratory analysis composed of two different experiments. Our results showed that our framework leads to a faster way of executing regionalization tasks in the experimental analysis, allowing us to observe a significant gain of AHR and RegK-Means over AZP in execution time, while showing better or similar results in other metrics, such as figure of merit.

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Section: Clustering Techniques Reviewmentioning

confidence: 99%

A framework for spatial regionalization composed of novel clustering‐based algorithms under spatial contiguity constraints

Miranda

Viterbo

Bernardini

2022

Transactions in GIS

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“…Then, the Canopy algorithm and the KMC were executed in parallel form to cluster the text data. The T1 and T2 values of the Canopy algorithm were set through cross-validation [22,23]. The Canopy algorithm was called to pre-cluster the preprocessed text data, producing the initial cluster centers.…”

Section: The Wck-means Text Clustering Algorithmmentioning

confidence: 99%

Design and Application of a Text Clustering Algorithm Based on Parallelized K-Means Clustering

Wang¹,

Zhou²,

Li³

2019

RIA

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The traditional text clustering algorithms face two common problems: the high dimensionality of computing vectors and poor calculation efficiency. To solve these problems, this paper explores deep into the K-means clustering (KMC), Hadoop and Spark big data technique, and then proposes a novel text clustering algorithm based on the KMC parallelized on big data platform. The propose algorithm is denoted as the SWCK-means. First, the Word2vec was adopted to calculate the weights of word vectors, and thus reduce the dimensionality of the massive text data. Next, the Canopy algorithm was introduced to cluster the weight data, and identify the initial cluster centers for the KMC. On this basis, the KMC was employed to cluster the preprocessed data. To improve the efficiency, a parallel design for the Canopy algorithm and the KMC was developed under the Spark architecture. The proposed algorithm was verified through experiments on a massive amount of online text data. The results show that our algorithm achieved more accurate classification effects than the traditional KMC, especially in handling a huge amount of data.

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Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines

2022

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User clustering based on Canopy+K-means algorithm in cloud computing

Cited by 8 publications

References 5 publications

A framework for spatial regionalization composed of novel clustering‐based algorithms under spatial contiguity constraints

A framework for spatial regionalization composed of novel clustering‐based algorithms under spatial contiguity constraints

Design and Application of a Text Clustering Algorithm Based on Parallelized K-Means Clustering

Automated Recognition Model of Geomechanical Information Based on Operational Data of Tunneling Boring Machines

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