A Parameter-Free Clustering Algorithm for Datasets with Missing Values

แนวคิดหลัก

SDC, a novel parameter-free clustering algorithm, can effectively cluster missing datasets by splitting dimensions, adapting decision graphs, and fusing cluster partitions without the need for imputation.

บทคัดย่อ

The paper proposes a novel clustering algorithm called SDC (Single-Dimensional Clustering) to handle missing datasets without any input parameters.

Key highlights:

1. SDC removes the imputation process and adapts the decision graph to missing datasets by splitting dimensions and using "partition intersection" fusion.
2. SDC introduces "gravity" to contract cluster boundaries, allowing single-dimensional datasets to inherit more information from the original dataset and enhance clustering effectiveness.
3. SDC designs a lightweight batch-density calculation method to significantly reduce the time complexity of decision graph generation and gravity calculation.
4. Experiments show that SDC outperforms baseline algorithms with multiple parameters by at least 13.7% (NMI), 23.8% (ARI), and 8.1% (Purity) on missing datasets, and its advantage remains consistent regardless of the increase in missing data rate.

สถิติ

Missing datasets are prevalent in the real world, where some objects have missing values in certain dimensions.
Existing clustering algorithms for missing datasets require input parameters for both the imputation and clustering processes, making it difficult to obtain accurate results.
The probabilities of achieving high and low accuracy for GAIN and MDIOT with different parameters indicate that these algorithms only have a small probability of obtaining high accuracy.

คำพูด

"Missing datasets are a representative class of specialized datasets where some objects have missing values in certain dimensions."
"Too many input parameters inevitably increase the difficulty of obtaining accurate clustering results."

ข้อมูลเชิงลึกที่สำคัญจาก

A parameter-free clustering algorithm for missing datasets

by Qi Li,Xianju... ที่ arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05363.pdf

สอบถามเพิ่มเติม

How can the proposed single-dimensional strategy in SDC be extended to handle other types of specialized datasets beyond missing datasets

The single-dimensional strategy in SDC can be extended to handle other types of specialized datasets by adapting the concept of dimension splitting and partition intersection to suit the specific characteristics of those datasets. For example, in datasets with high-dimensional features, the single-dimensional strategy can be modified to consider subsets of dimensions rather than individual dimensions. This way, the clustering process can be performed on these subsets to capture the underlying patterns in the data. Additionally, for datasets with categorical features, the strategy can be adjusted to handle the unique nature of categorical data by defining appropriate distance metrics or similarity measures for clustering.

What are the potential limitations or drawbacks of the "partition intersection" fusion method used in SDC, and how could it be further improved

The "partition intersection" fusion method used in SDC may have limitations in scenarios where the clusters are not well-separated or when there is significant overlap between clusters. In such cases, the fusion process may lead to misclassification of objects or inaccurate cluster boundaries. To improve this method, one approach could be to incorporate a weighting mechanism that considers the confidence level of each cluster assignment based on the density or proximity of objects. By assigning weights to different clusters during the fusion process, the algorithm can better capture the uncertainty in cluster assignments and make more informed decisions.

What other techniques or approaches beyond decision graphs could be explored to eliminate input parameters in clustering algorithms for missing datasets or other complex data scenarios

Beyond decision graphs, other techniques that could be explored to eliminate input parameters in clustering algorithms for missing datasets or complex data scenarios include:

AutoML Approaches: Leveraging automated machine learning (AutoML) techniques to automatically select the optimal parameters for clustering algorithms based on the characteristics of the dataset. This can involve hyperparameter tuning, model selection, and feature engineering to optimize clustering performance.
Meta-Learning: Utilizing meta-learning frameworks to learn the best parameter settings for clustering algorithms across different datasets. By training a meta-learner on a diverse set of datasets, the algorithm can adapt and generalize well to new datasets without the need for manual parameter tuning.
Ensemble Methods: Employing ensemble methods to combine multiple clustering algorithms with different parameter settings. By aggregating the results of individual algorithms, ensemble methods can mitigate the impact of parameter sensitivity and improve overall clustering accuracy.
Deep Learning: Exploring deep learning models, such as neural networks, for clustering tasks with missing data. Deep learning architectures can learn complex patterns and relationships in the data, potentially reducing the reliance on manual parameter tuning and improving clustering performance in challenging scenarios.