# GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Image Conversion Stage

#### 2.1.1. Conversion from Time-Series into Grayscale Image

#### 2.1.2. Conversion from Time-Series into RP Images

#### 2.1.3. Conversion from Time-Series into MTF Images

#### 2.1.4. Conversion from Time-Series to Gramian Angular Difference Field

#### 2.2. Image Enhancement Stage

#### 2.3. Image Clustering Stage

## 3. Experiment and Results

#### 3.1. Datasets and Evaluation Index

#### 3.2. Conversion Result of Two-Dimensional Images

#### 3.3. Comparative Analysis of Clustering Results

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Dataset | Description | Train Size | Test Size | Length | Classes |
---|---|---|---|---|---|

BME | Beef spectrograms, from pure beef and beef adulterated with varying degrees of offal. | 30 | 150 | 128 | 3 |

CBF | The data for each class is standard normal noise plus different offset terms. | 30 | 900 | 128 | 3 |

SmoothSubspace | Each time-series contains a continuous subspace spanning 5 continuous timestamps, which is used to test whether smooth subspace can be extracted. | 150 | 150 | 15 | 3 |

SonyAIBORobotSurface2 | This dataset contains different accelerometers for robot walking in cement or carpet/field. | 27 | 953 | 65 | 2 |

SyntheticControl | Six different categories of control charts, including normal, cyclic, increasing trend, decreasing trend, upward shift, and downward shift. | 300 | 300 | 60 | 6 |

UMD | A synthetic dataset with three types: upper, lower, and no bells arising at the initial or final cycle. | 36 | 144 | 150 | 3 |

Image Type | BME | CBF | SmoothSubspace | SonyABR-obotSface2 | SyntheticControl | UMD |
---|---|---|---|---|---|---|

Grayscale | 91.18 | 44.32 | 22.89 | 23.50 | 26.26 | 41.50 |

RP | 33.14 | 58.30 | 39.13 | 27.12 | 65.63 | 33.93 |

MTF | 38.54 | 53.26 | 12.74 | 21.88 | 51.08 | 12.29 |

GADF | 91.18 | 54.77 | 42.43 | 25.74 | 71.45 | 46.53 |

Dataset | SAE +K-Means [31] | AE +K-Means [32] | AE_Conv +K-Means | |||

Index | NMI | ACC | NMI | ACC | NMI | ACC |

BME | 22.37 | 55.56 | 20.62 | 50.69 | 16.27 | 55.56 |

CBF | 27.69 | 51.08 | 23.24 | 48.39 | 26.89 | 58.47 |

SmoothSubspce | 6.21 | 50.00 | 8.13 | 50.83 | 19.64 | 75.00 |

SonyAIBORobotSurface2 | 24.29 | 71.43 | 0.19 | 67.22 | 13.34 | 64.29 |

SyntheticControl | 35.71 | 47.50 | 15.68 | 28.12 | 15.78 | 39.58 |

UMD | 26.78 | 52.78 | 14.30 | 46.53 | 18.63 | 55.56 |

Dataset | DEC [22] | DEC_FCN | GW_DC | |||

Index | NMI | ACC | NMI | ACC | NMI | ACC |

BME | 46.69 | 58.33 | 44.54 | 52.78 | 91.18 | 61.13 |

CBF | 30.32 | 76.88 | 32.77 | 63.44 | 54.77 | 80.65 |

SmoothSubspce | 28.01 | 53.33 | 23.37 | 51.67 | 42.43 | 65.00 |

SonyAIBORobotSurface2 | 30.17 | 79.08 | 14.30 | 76.02 | 25.74 | 77.04 |

SyntheticControl | 24.72 | 53.33 | 52.82 | 40.00 | 71.45 | 66.67 |

UMD | 33.74 | 63.89 | 16.46 | 66.67 | 46.53 | 75.00 |

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**MDPI and ACS Style**

Li, X.; Li, T.; Wang, Y.
GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement. *Algorithms* **2021**, *14*, 349.
https://doi.org/10.3390/a14120349

**AMA Style**

Li X, Li T, Wang Y.
GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement. *Algorithms*. 2021; 14(12):349.
https://doi.org/10.3390/a14120349

**Chicago/Turabian Style**

Li, Xutong, Taoying Li, and Yan Wang.
2021. "GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement" *Algorithms* 14, no. 12: 349.
https://doi.org/10.3390/a14120349