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  1. AU="Vardakas, Georgios"
  2. AU="Fogg, Ryan"
  3. AU="Viviane M. Parra"
  4. AU="Kushner, Adam"
  5. AU="Claude Pasquier"
  6. AU="Guomin Zhang"
  7. AU=van der Donk Lieve E H
  8. AU="Reynaerts, Audrey"
  9. AU="Alberts, Susan C"
  10. AU="Kosicki, Jakub Z"
  11. AU=Eifling Michael
  12. AU="Xing, Xinxin"
  13. AU="Baigun, Claudio"
  14. AU="Abu-Hamad, Ghassan"
  15. AU="Mulla, Zuber D"
  16. AU="Schröder, H"
  17. AU=Ruiz Michael Anthony
  18. AU="Kemmoku, Haruka"
  19. AU="Meseguer, M"
  20. AU="Pillaye, Jayshree"
  21. AU="Andrew Pettitt"
  22. AU="Malawski, M"
  23. AU=Marhofer P
  24. AU=Mandel H G
  25. AU="Duffy, Richard"
  26. AU=Kaseb Hatem AU=Kaseb Hatem
  27. AU=Kong Tak?kwan AU=Kong Tak?kwan
  28. AU=Nagaraja Sridevi
  29. AU="Bu, Yingzi"
  30. AU=Seddighi Hamed AU=Seddighi Hamed
  31. AU="De Keyser, Johan"
  32. AU="Zhenqiang Bi"
  33. AU=Wang Jun
  34. AU=Zhang Fuping
  35. AU="Shatilov, D N"

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  1. Buch ; Online: Silhouette Aggregation

    Vardakas, Georgios / Pavlopoulos, John / Likas, Aristidis

    From Micro to Macro

    2024  

    Abstract: Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment. To assess the quality of the clustering of the whole dataset, the scores of all the ... ...

    Abstract Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment. To assess the quality of the clustering of the whole dataset, the scores of all the points in the dataset are either (micro) averaged into a single value or averaged at the cluster level and then (macro) averaged. As we illustrate in this work, by using a synthetic example, the micro-averaging strategy is sensitive both to cluster imbalance and outliers (background noise) while macro-averaging is far more robust to both. Furthermore, the latter allows cluster-balanced sampling which yields robust computation of the silhouette score. By conducting an experimental study on eight real-world datasets, estimating the ground truth number of clusters, we show that both coefficients, micro and macro, should be considered.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 310
    Erscheinungsdatum 2024-01-11
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: Deep Clustering Using the Soft Silhouette Score

    Vardakas, Georgios / Papakostas, Ioannis / Likas, Aristidis

    Towards Compact and Well-Separated Clusters

    2024  

    Abstract: Unsupervised learning has gained prominence in the big data era, offering a means to extract valuable insights from unlabeled datasets. Deep clustering has emerged as an important unsupervised category, aiming to exploit the non-linear mapping ... ...

    Abstract Unsupervised learning has gained prominence in the big data era, offering a means to extract valuable insights from unlabeled datasets. Deep clustering has emerged as an important unsupervised category, aiming to exploit the non-linear mapping capabilities of neural networks in order to enhance clustering performance. The majority of deep clustering literature focuses on minimizing the inner-cluster variability in some embedded space while keeping the learned representation consistent with the original high-dimensional dataset. In this work, we propose soft silhoutte, a probabilistic formulation of the silhouette coefficient. Soft silhouette rewards compact and distinctly separated clustering solutions like the conventional silhouette coefficient. When optimized within a deep clustering framework, soft silhouette guides the learned representations towards forming compact and well-separated clusters. In addition, we introduce an autoencoder-based deep learning architecture that is suitable for optimizing the soft silhouette objective function. The proposed deep clustering method has been tested and compared with several well-studied deep clustering methods on various benchmark datasets, yielding very satisfactory clustering results.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2024-02-01
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Buch ; Online: Global $k$-means$++$

    Vardakas, Georgios / Likas, Aristidis

    an effective relaxation of the global $k$-means clustering algorithm

    2022  

    Abstract: The $k$-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global $k$-means is a deterministic ... ...

    Abstract The $k$-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global $k$-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but its well-known that requires high computational cost. It partitions the data to $K$ clusters by solving all $k$-means sub-problems incrementally for all $k=1,\ldots, K$. For each $k$ cluster problem, the method executes the $k$-means algorithm $N$ times, where $N$ is the number of datapoints. In this paper, we propose the \emph{global $k$-means\texttt{++}} clustering algorithm, which is an effective way of acquiring quality clustering solutions akin to those of global $k$-means with a reduced computational load. This is achieved by exploiting the center selection probability that is effectively used in the $k$-means\texttt{++} algorithm. The proposed method has been tested and compared in various benchmark datasets yielding very satisfactory results in terms of clustering quality and execution speed.
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-11-22
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: UniForCE

    Vardakas, Georgios / Kalogeratos, Argyris / Likas, Aristidis

    The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters

    2023  

    Abstract: Estimating the number of clusters k while clustering the data is a challenging task. An incorrect cluster assumption indicates that the number of clusters k gets wrongly estimated. Consequently, the model fitting becomes less important. In this work, we ... ...

    Abstract Estimating the number of clusters k while clustering the data is a challenging task. An incorrect cluster assumption indicates that the number of clusters k gets wrongly estimated. Consequently, the model fitting becomes less important. In this work, we focus on the concept of unimodality and propose a flexible cluster definition called locally unimodal cluster. A locally unimodal cluster extends for as long as unimodality is locally preserved across pairs of subclusters of the data. Then, we propose the UniForCE method for locally unimodal clustering. The method starts with an initial overclustering of the data and relies on the unimodality graph that connects subclusters forming unimodal pairs. Such pairs are identified using an appropriate statistical test. UniForCE identifies maximal locally unimodal clusters by computing a spanning forest in the unimodality graph. Experimental results on both real and synthetic datasets illustrate that the proposed methodology is particularly flexible and robust in discovering regular and highly complex cluster shapes. Most importantly, it automatically provides an adequate estimation of the number of clusters.
    Schlagwörter Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Methodology
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2023-12-18
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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