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Representing Unequal Data Series in Vector Space with Its Application in Bank Customer Clustering

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High-Performance Computing and Big Data Analysis (TopHPC 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 891))

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Abstract

Data Series are one of the major types of data producing in different domains. Performing data analysis on this domain currently have two obstacles. The big volume of data and also the large dimensionality and unequal size. In this research we have employed two famous methods from NLP algorithms named Word2Vec and Doc2Vec to represent and compress data series at the same time. These two approaches are based on Feed Forward Neural Networks. We have used a private bank dataset with more than 11 million transactions and converted it to data series. Then we employed two representation methods to convert each time series to a fix size [1 × 100] vector. The first approach ignores the order in data series and gains more than 96% compression ratio (30:1). The second method preserves order of data to some extent and gets about 24% compression ratio (1.35:1). The other advantage of proposed method is the fix size vector representation which makes the comparison of two data series with different length easily possible. The k-means clustering algorithm is performed on Bank Customer Data Series to show a usage of proposed data series representation.

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Notes

  1. 1.

    Word2Vec Data Series Representation.

  2. 2.

    Doc2Vec Data Series Representation.

  3. 3.

    There is another version of Word2Vec in which the input is just w(t) and the goal is to predict n surrounding words. That version is called Skipped Gram.

  4. 4.

    We do not take into account non-financial transaction.

  5. 5.

    In elbow method the k-means clustering is done on different k values, when the average of minimum distance error seize to reduce as the amount of k increases the optimum value of k is obtained.

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Authors and Affiliations

  1. Business Intelligence Group, Informatics Services Corporation, Tehran, Iran

    Shohreh Tabatabayi Seifi & Ahmad Ali Ekhveh

Authors
  1. Shohreh Tabatabayi Seifi
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  2. Ahmad Ali Ekhveh
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Correspondence to Shohreh Tabatabayi Seifi .

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Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Lucio Grandinetti

  2. Kharazmi University, Tehran, Iran

    Seyedeh Leili Mirtaheri

  3. University of Calabria, Rende, Italy

    Reza Shahbazian

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Tabatabayi Seifi, S., Ekhveh, A.A. (2019). Representing Unequal Data Series in Vector Space with Its Application in Bank Customer Clustering. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-33495-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33494-9

  • Online ISBN: 978-3-030-33495-6

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