Data stream clustering by divide and conquer approach based on vector model

Khalilian, Madjid; Mustapha, Norwati; Sulaiman, Nasir

doi:10.1186/s40537-015-0036-x

Cited by 50 publications

(11 citation statements)

References 28 publications

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“…Methods and techniques Article SPADE [132] Locally supervised metric learning (LSML) [133] KTS [106] Multinomial latent dirichlet allocation [106] Voltage clustering algorithm [106] Locality sensitive hashing (LSH) [134] User profile vector update algorithm [134] Tag assignment stream clustering (TASC) [134] StreamMap [117] Density cognition [117] QRS detection algorithm [87] Forward chaining rule [110] Stream [135] CluStream [136,137] HPClustering [138] DenStream [139] D-Stream [140] ACluStream [141] DCStream [142] P-Stream [143] ADStream [144] Continuous query processing (CQR) [145] FPSPAN-growth [146] Outlier method for cloud computing algorithm (OMCA) [147] Multi-query optimization strategy (MQOS) [148] Parallel K-means clustering [72] Visibly push down automata (VPA) [73] Incremental MI outlier detection algorithm (Inc I-MLOF) [149] Adaptive windowing based online ensemble (AWOE) [74] Dynamic prime-number based security verification [84] K-anonymity, I-diversity, t-closeness [90] Singular spectrum matrix completion (SS-MC) [76] Temporal fuzzy concept analysis [96] ECM-sketch [77] Nearest neighbour [91] Markov chains [91] Block-QuickSort-AdjacentJobMatch [86] Block-QuickSort-OverlapReplicate…”

Section: Table 8 Methods and Techniques For Big Data Stream Analysismentioning

confidence: 99%

Big data stream analysis: a systematic literature review

2019

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Advances in information technology have facilitated large volume, high-velocity of data, and the ability to store data continuously leading to several computational challenges. Due to the nature of big data in terms of volume, velocity, variety, variability, veracity, volatility, and value [1] that are being generated recently, big data computing is a new trend for future computing. Big data computing can be generally categorized into two types based on the processing requirements, which are big data batch computing and big data stream computing Abstract Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. Only a few big data streaming tools and technologies can do all of the batch, streaming, and iterative jobs; there seems to be no big data tool and technology that offers all the key features required for now and standard benchmark dataset for big data streaming analytics has not been widely adopted. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream computing mode, effective resource allocation strategy and parallelization issues to cope with the ever-growing size and complexity of data.

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Section: Table 8 Methods and Techniques For Big Data Stream Analysismentioning

confidence: 99%

Big data stream analysis: a systematic literature review

2019

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“…We used three datasets and they are KDD CUP'99 [4], [5], forest cover type [5], [26], electric power consumption dataset [26]. We compared the accuracy, precision, recall and F-Measures on these datasets.…”

Section: Datasets Descriptionmentioning

confidence: 99%

“…In recent years, multiple organizations generate huge amounts of data. Data stream application domains includes information analysis in network data flow monitoring, Internet of Things (IoT) applications regularly sending sensors data, web page access and web click information, weather forecasting information and the economic information produced by finance and securities companies and so on [1][2][3][4][5]. Conventional data mining methods mostly focused on mining static and memory resident data repositories.…”

Section: Introductionmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Data Stream Clustering

Adepu¹,

Gyani²,

Narsimha³

2019

IJECE

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A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.

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“…Şekil 13. DCSTREAM algoritmasına ait framework [74] SPE-Cluster [80], oto-regresyon modelleme tekniğini kullanarak akan veriler arasındaki korelasyonu hesaplar. Bunun için akan veriden birbiri ile alakalı nitelikleri bulmak için frekans spektrumunu bulur.…”

Section: A çEkirdek Mikro-kümeler B Birleştirilmiş Mikro-kümelerunclassified

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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Data stream clustering by divide and conquer approach based on vector model

Cited by 50 publications

References 28 publications

Big data stream analysis: a systematic literature review

Big data stream analysis: a systematic literature review

An Improved Differential Evolution Algorithm for Data Stream Clustering

Akan Veri Kümeleme Teknikleri Üzerine Bir Derleme

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