New approach for distributed clustering

Ghanem, Souhila; Kechadi, Tahar; Tari, Abdelkamel

doi:10.1109/icsdm.2011.5969005

Cited by 5 publications

(4 citation statements)

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“…We developed a Parallel Clustering Approach (ParCA) for spacial datasets, 8 which is composed by two main phases: local clustering phase and global clustering phase . Assume that the dataset is already distributed among the system nodes; during the first phase, each processing node executes a clustering algorithm on its local dataset to produce local clusters.…”

Section: Methodsmentioning

confidence: 99%

“…(ii) RoI extraction using a parallel clustering approach , which exploits a parallel DBSCAN 7 implementation on grouped social media items to identify RoIs efficiently. The parallel clustering is based on ParCA, 8 a parallel approach for clustering spatial datasets. ParCA proved its efficiency using syntactic and benchmark datasets only.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this paper is a new methodology based on parallel computations for extracting RoIs from social media data, which is composed of two main steps: i) Automatic keywords extraction and data grouping, for finding keywords that identify the places of interests; these keywords will be used to group social media items according to the places they refer to; ii) RoIs extraction using a parallel clustering approach, which exploits a parallel DBSCAN 7 implementation on grouped social media items to identify RoIs efficiently. The parallel clustering is based on ParCA 8 , a parallel approach for clustering spatial datasets.…”

Section: Introductionmentioning

confidence: 99%

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Parallel extraction of Regions‐of‐Interest from social media data

Belcastro

Kechadi²,

Marozzo

et al. 2020

Concurrency and Computation

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Geotagged data gathered from social media can be used to discover places-of-interest (PoIs) that have attracted many visitors. Since a PoI is generally identified by geographical coordinates of a single point, it is hard to match it with people trajectories. Therefore, we define an area, called region-of-interest (RoI), represented by the boundaries of a PoI. The main goal of this study is to discover RoIs from PoIs using spatial data mining techniques. In this paper, we propose a new parallel method for extracting RoIs from social media datasets. It consists of two main steps: (i) automatic keyword extraction and data grouping and (ii) parallel RoI extraction. The first step extracts keywords identifying the PoIs; these keywords are used to group social media items according to the places they refer to. The second step uses a Parallel Clustering Approach (ParCA) of spatial dataset to identify RoIs. ParCA exploits a parallel execution of DBSCAN on subsets of data to generate subclusters on each processing node and then merge overlapping subclusters to form global clusters. ParCA was implemented using the MapReduce model. Experiments performed over a set of PoIs in the city of Rome using social media data show that our approach is highly scalable and reaches an accuracy of 79% in detecting RoIs. On a parallel computer with 50 cores, we obtained a speedup of 52 by processing large datasets divided into 32 splits, compared with the execution time registered when each dataset is not partitioned.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parallel extraction of Regions‐of‐Interest from social media data

Belcastro

Kechadi²,

Marozzo

et al. 2020

Concurrency and Computation

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“…To consider handling distributed datasets for the clustering problem, we should propose distributed clustering methods and they should be divided into horizontal and vertical methods, or homogeneous and heterogeneous distributed clustering algorithms, with respect to the type of dataset. Most distributed clustering algorithms are homogeneous algorithms, including those of [6,7] and are an extension of parallel computing. On the other hand, [8] first proposed the collective hierarchical clustering (CHC) algorithm to consider the distributed clustering problem in heterogeneous databases.…”

Section: Introductionmentioning

confidence: 99%

Double Deep Autoencoder for Heterogeneous Distributed Clustering

Chen

Huang

2019

Information

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Given the issues relating to big data and privacy-preserving challenges, distributed data mining (DDM) has received much attention recently. Here, we focus on the clustering problem of distributed environments. Several distributed clustering algorithms have been proposed to solve this problem, however, previous studies have mainly considered homogeneous data. In this paper, we develop a double deep autoencoder structure for clustering in distributed and heterogeneous datasets. Three datasets are used to demonstrate the proposed algorithms, and show their usefulness according to the consistent accuracy index.

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