SNCStream+: Extending a high quality true anytime data stream clustering algorithm

Barddal, Jean Paul; Gomes, Heitor Murilo; Enembreck, Fabrício; Barthès, Jean-Paul

doi:10.1016/j.is.2016.06.007

Cited by 23 publications

(22 citation statements)

References 25 publications

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“…SNCStream [11] is an online clustering algorithm capable of finding non-hyper-spherical clusters. SNCStream, in contrast to other data stream clustering algorithms, uses only 1-step processing to find clusters by using a social network generation and evolution model, which is based on homophily, it uses a scale-free-like homophily procedure to track the evolution of clusters during data streams.…”

Section: 4mentioning

confidence: 99%

“…SNCStream, in contrast to other data stream clustering algorithms, uses only 1-step processing to find clusters by using a social network generation and evolution model, which is based on homophily, it uses a scale-free-like homophily procedure to track the evolution of clusters during data streams. SNCStream + [12] is another high-quality real-time data stream clustering algorithm and an extension of SNCStream [11] algorithm. SNCStream + adapts the characteristics of the SNCStream algorithm and this method is more efficient as it executes in decreased complexity in the average case.…”

Section: 4mentioning

confidence: 99%

See 1 more Smart Citation

An evaluation of data stream clustering algorithms

Mansalis

Ntoutsi

Pelekis

et al. 2018

Statistical Analysis

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Data stream clustering is a hot research area due to the abundance of data streams collected nowadays and the need for understanding and acting upon such sort of data. Unsupervised learning (clustering) comprises one of the most popular data mining tasks for gaining insights into the data. Clustering is a challenging task, while clustering over data streams involves additional challenges such as the single pass constraint over the raw data and the need for fast response. Moreover, dealing with an infinite and fast changing data stream implies that the clustering model extracted upon such sort of data is also subject to evolution over time. Several stream clustering surveys exist already in the literature; however, they focus on a theoretical presentation of the surveyed algorithms. On the contrary, in this paper, we survey the state‐of‐the‐art stream clustering algorithms and we evaluate their performance in different data sets and for different parameter settings.

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Section: 4mentioning

confidence: 99%

Section: 4mentioning

confidence: 99%

An evaluation of data stream clustering algorithms

Mansalis

Ntoutsi

Pelekis

et al. 2018

Statistical Analysis

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show abstract

“…Kullanıcıya ait click verisi analizi [6], saldırı tespit sistemleri [7][8][9], sosyal medya [10][11][12], finansal uygulamalar [13], bilimsel araştırmalar [14], sağlık araştırmaları [15][16][17], mobil uygulamalar [18], nesnelerin interneti (IoT) [19] ve sensor ağ [20,21] gibi pek çok alanda kullanılmaktadır. Nesnelerin interneti konusunun yaygınlaştığı günümüzde uygulama alanlarının daha da artacağını söylemek mümkündür.…”

Section: Akan Veri Kümeleme Yaklaşımlarının Uygulama Alanlarıunclassified

Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Şenol

Karacan

2018

European Journal of Science and Technology

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Öz Günümüz teknolojisinin gelişmesine paralel olarak bilgisayar ortamına aktarılmış olan veri miktarı inanılmaz boyutlara ulaşmış ve gün geçtikçe de artmaktadır. Bu nedenle veriyi işleme yöntemleri de değişmektedir. Klasik kümeleme yaklaşımlarında veri statiktir. Oysa günümüz teknolojisinde, verinin çok hızlı olduğu dünyada artık veriyi akarken kümeleyecek, kullanıcıya istediği zaman sonuç verebilecek uygulamalara ihtiyaç vardır. Bu anlamda ihtiyacı karşılayan akan veri kümeleme yaklaşımlarına olan talep gün geçtikçe artmaktadır. Çünkü akan veri kümeleme yaklaşımları bir defa okumalı, hızlı ve kendisini yeni gelen veriye uyarlama özelliğine sahiptir. Yani veri bir yandan akarken bir yandan kullanıcıya sonuç üretilebilmektedir. Bu çalışmada akan veri kümeleme alanında yapılan çalışmalar derlenmekte ve bu alana ilgi duyan araştırmacılara ışık tutulmaktır.

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“…However, each of the above datasets, as well as most datasets used in previous works on streaming clustering, is not originally a streaming dataset; the entries are only read in some order and consumed as a stream. To better model the evolving nature of data streams and the drifting of center locations, we generate a semi-synthetic dataset, called Drift, which we derive from the USCensus1990 dataset [17] as follows: The method is inspired by Barddal [18]. The first step is to cluster the USCensus1990 dataset to compute 20 cluster centers and for each cluster, the standard deviation of the distances to the cluster center.…”

Section: Datasetsmentioning

confidence: 99%

Streaming k-Means Clustering with Fast Queries

Tangwongsan

Tirthapura

2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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We present methods for k-means clustering on a stream with a focus on providing fast responses to clustering queries. Compared to the current state-of-the-art, our methods provide substantial improvement in the query time for cluster centers while retaining the desirable properties of provably small approximation error and low space usage. Our algorithms rely on a novel idea of "coreset caching" that systematically reuses coresets (summaries of data) computed for recent queries in answering the current clustering query. We present both theoretical analysis and detailed experiments demonstrating their correctness and efficiency.

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SNCStream+: Extending a high quality true anytime data stream clustering algorithm

Cited by 23 publications

References 25 publications

An evaluation of data stream clustering algorithms

An evaluation of data stream clustering algorithms

Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

Streaming k-Means Clustering with Fast Queries

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