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Filtering by: Creator Shan, Liren Resource type Dissertation

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Approximation Algorithms for Explainable Clustering

Description:

Clustering is a fundamental task in unsupervised learning, which aims to partition the data set into several clusters. It is widely used for data mining, image segmentation, and natural language processing. One of the most popular clustering methods is centroid-based clustering, including k-medians and k-means clustering. k-medians and k-means clustering...

Keyword:

Decision Tree, Clustering, Explainability, and k-means

Subject:

Computer science

Creator:

Shan, Liren

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

08/23/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

http://dissertations.umi.com/northwestern:16717 and etdadmin_upload_1012370