Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

Kanmaz, Merve; Aydın, Muhammed Ali

doi:10.17341/gazimmfd.460495

Cited by 2 publications

(2 citation statements)

References 15 publications

(14 reference statements)

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“…Günümüzde kullanılan akıllı telefonlar, tabletler ve sensörler gibi teknolojik cihazlar ile Twitter, Instagram, Facebook gibi sosyal ağlar bilgisayar ortamına aktarılan veri miktarında çok önemli bir artışa neden olmaktadır [1][2][3][4][5]. Veri miktarının bu kadar arttığı bir ortamda artık klasik veri işleme yöntemleri yetersiz kalmaktadır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

Şenol

Karacan

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Highlights:Graphical/Tabular Abstract  Fully online data stream clustering  Evolutionary based clustering  Adaptive radius  Time based summarization  Memory for past status of clusters Figure A. Comparision of clustering quality and run-time complexity of algorithms on KDD datasetPurpose: The aim of this article to propose a new data stream clustering algorithm, which has an adaptive radius, can adapt itself to the evolutionary structure of streaming data and works in a fully online manner. Theory and Methods:In this study, kd-tree is used to forming and splitting clusters, adaptive radius approach is used to support increasing and decreasing the size of clusters, active/inactive status of clusters is used to adapt to the evolutionary structure of streaming data and all the operations are done online. In order to create a new cluster, the data that does not belong to any cluster are placed in a kd-tree, and the rangesearch operation is performed on those data according to predefined variables r (the radius of candidate cluster) and N (the number of data must be in the area). After forming the clusters, the radius of each cluster could be increased or decreased over time if necessary. Some clusters may be split and some may be merged over time because of dynamically changing structure of streaming data. Inactivation and reactivation of the status of clusters is used to allow for the identification of clusters formed in the same region at a different time interval with same cluster labels in accordance with the nature of the streaming data contrary to literature. This feature increases clustering quality of the proposed method. A summarization method that consist of time window and sliding window is used to support time based summarization without reduce performance. Results:To verify the effectiveness of KD-AR Stream algorithm, it is compared with SE-Stream, DPStream, and CEDAS on a variety of well-known datasets in terms of clustering quality and run-time complexity. The results show that KD-AR Stream outperforms other algorithms with a higher clustering success in a reasonable time as shown in Fig. A. Conclusion:The aim of this study is to propose a novel data stream clustering algorithm that adapts to the dynamic structure of the streaming data. The aim achieved by using the five evolutionary process which are appearance, activation/inactivation, self-evolution, merge, and split. According to the results, the proposed method is very successful in terms of clustering quality and run-time complexity.

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Section: Gi̇ri̇ş (Introduction)unclassified

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

Şenol

Karacan

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

View full text Add to dashboard Cite

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“…Teknolojinin gelişmesinin ve internet hızının artmasının yanında; akıllı telefon, tablet ve bilgisayarlar gibi elektronik cihazlar ile Facebook, Twitter ve Instagram gibi sosyal medya ortamlarının kullanımının yagınlaşmasıyla berbaber bilgisayar ortamına aktarılan veri miktarında çok ciddi bir artış söz konusudur (AlNuaimi, Masud, Serhani ve Zaki, 2019;Görmüş, Aydın ve Ulutaş, 2018;Kanmaz ve Aydin, 2018;Martín, Julián ve Cos-Gayón, 2019;Oussous, Benjelloun, Ait Lahcen ve Belfkih, 2018). Bu nedenle klasik veri madenciliği yöntemleri artık yetersiz kalmaktadır.…”

Section: Introductionunclassified

ImpSlidingWindow: Kayan Pencere Tabanlı Akan Veri Özetleme Yönteminin Performansını Arttırmaya Yönelik Yeni Bir Model

Şenol¹,

Karacan²

2019

European Journal of Science and Technology

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Sliding window based data summarization which is a quantity based summarization is commonly used in data stream clustering area in which the recent data is more important. In this data summarization method, w which is a predefined variable, of the most recent data is taken as the summary each time a new data arrives and the window slides one by one. This means that the model processes all the data in the data window each time a new data arrives. This approach causes the performance to reduce. Therefore, there is a need of new studies to be proposed in this area. In this study, a new sliding window model named ImpSlidingWindow (ISW) is proposed as a solution to the mentioned problem. In the proposed model, we propose that clustering model to work whenever a certain number of data accumulates instead of each data entry. With this new model, the sliding window width is divided into four equal parts and the clustering model works at the end of each part. As a result, a significant increase in the performance is achieved by enabling the clustering model to run four times instead of working as much as the number of data in the window width. When the proposed model applied to KD-AR Stream algorithm which is a proposed algorithm in the data stream clustering area, it has been found that up to 80% improvement obtained in run-time complexity.

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Kablosuz Sensör Ağlarda Konumlandırma Yöntemleri ve K-means++ Kümeleme Yöntemi ile Yeni Yaklaşım

Cited by 2 publications

References 15 publications

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

Kd-tree and adaptive radius (KD-AR Stream) based real-time data stream clustering

ImpSlidingWindow: Kayan Pencere Tabanlı Akan Veri Özetleme Yönteminin Performansını Arttırmaya Yönelik Yeni Bir Model

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