A Distributed Genetic Algorithm for Graph-Based Clustering

Búza, Krisztián; Buza, Antal; Kis, Piroska B.

doi:10.1007/978-3-642-23169-8_35

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Recently a huge number of clustering algorithms were developed for various tasks see e.g. [8], [9] and [10]. For surveys on clustering please consider [15] and [16].…”

Section: Imentioning

confidence: 99%

Partitional clustering of tick data to reduce storage space

Nagy

Búza

2012

2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES)

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Tick data is one of the most prominent types of temporal data, as it can be used to represent data in various domains such as geophysics or finance. Storage of tick data is a challenging problem because two criteria have to be fulfilled simultaneously: the storage structure should allow fast execution of queries and the data should not occupy too much space on the hard disk or in the main memory. In this paper, we present a clustering-based solution, and we introduce a new clustering algorithm, SOPAC, that is designed to support the storage of tick data. Our approach is based on the search for a partitional clustering that optimizes storage space. We evaluate our algorithm both on publicly available real-world datasets, as well as real-world tick data from the financial domain. We also investigate on task-specific benchmarks, how well our approach estimates the optimum. Our experiments show that, for the tick data storage problem, our algorithm substantially outperformsboth in terms of statistical significance and practical relevance -state-of-the-art clustering algorithms.

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“…Recently a huge number of clustering algorithms were developed for various tasks see e.g. [8], [9] and [10]. For surveys on clustering please consider [15] and [16].…”

Section: Imentioning

confidence: 99%

Partitional clustering of tick data to reduce storage space

Nagy

Búza

2012

2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES)

Self Cite

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“…In the last decades, very large number of clustering algorithms were developed for various tasks (see e.g. [6], [9], [10] and [12]). We refer to [19] and [22] for excellent surveys of clustering algorithms.…”

Section: Related Workmentioning

confidence: 99%

Storage-optimizing clustering algorithms for high-dimensional tick data

Búza

Nagy

Νανόπουλος

2014

Expert Systems with Applications

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Tick data are used in several applications that need to keep track of values changing over time, like prices on the stock market or meteorological measurements. Due to the possibly very frequent changes, the size of tick data tends to increase rapidly. Therefore, it becomes of paramount importance to reduce the storage space of tick data while, at the same time, allowing queries to be executed efficiently. In this paper, we propose an approach to decompose the original tick data matrix by clustering their attributes using a new clustering algorithm called Storage-Optimizing Hierarchical Agglomerative Clustering (SOHAC). We additionally propose a method for speeding up SOHAC based on a new lower bounding technique that allows SOHAC to be applied to highdimensional tick data. Our experimental evaluation shows that the proposed approach compares favorably to several baselines in terms of compression. Additionally, it can lead to significant speedup in terms of running time.

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“…Taking the nonuniform storage space requirement of the cells into account leads to a more realistic compression model with better compression rate. Second, SOHAC is a heuristic algorithm [9] which finds a good suboptimal clustering for tick data. In other words, not all possible clusters but some are considered in order to find the one with the best storage space (best compression ratio).…”

Section: Introductionmentioning

confidence: 99%

An efficient storage-optimizing tick data clustering model

Amintoosi¹,

Torshiz²,

Forghani³

et al. 2020

Turk J Elec Eng & Comp Sci

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Tick data is a large volume of data, related to a phenomenon such as stock market or weather change, with data values changing rapidly over time. An important issue is to store tick data table in a way that it occupies minimum storage space while at the same time it can provide fast execution of queries. In this paper, a mathematical model is proposed to partition tick data tables into clusters with the aim of minimizing the required storage space. The genetic algorithm is then used to solve the mathematical model which is indeed a clustering model. The proposed method has been evaluated on a real-world weather tick dataset and compared to the storage-optimizing hierarchical agglomerative clustering (SOHAC) algorithm. The experiments show that our proposed method substantially outperforms SOHAC in achieving smaller values for compression ratio while reducing the execution time for small number of clusters.

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A Distributed Genetic Algorithm for Graph-Based Clustering

Cited by 6 publications

References 6 publications

Partitional clustering of tick data to reduce storage space

Partitional clustering of tick data to reduce storage space

Storage-optimizing clustering algorithms for high-dimensional tick data

An efficient storage-optimizing tick data clustering model

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