| _version_ | 1866902339575611392 |
|---|---|
| author | Emerging Trends in Digital Transformation |
| author_facet | Emerging Trends in Digital Transformation |
| contents | <p><span>One important unsupervised learning technique in data mining is the K-means clustering method. This method efficiently organizes big datasets by splitting objects into k separate clusters. It is possible to provide clearer data classification by ensuring that items in the same cluster are more similar than things in other clusters. The first step in creating clusters is to pick data points at random, ensuring that each one has an equal number of items. Improving K-means clustering's ability to handle a wide variety of data types and guarantee fair weight distribution, this research presents a new method for choosing the best cluster from uniform and non-uniform datasets.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16900105 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | GENERALIZED K-MEANS CLUSTERING WITH CENTROID ENHANCEMENT: A STEP TOWARD ADAPTIVE DATA SEGMENTATION Emerging Trends in Digital Transformation Clustering InitialCentroids k-means Algorithm <p><span>One important unsupervised learning technique in data mining is the K-means clustering method. This method efficiently organizes big datasets by splitting objects into k separate clusters. It is possible to provide clearer data classification by ensuring that items in the same cluster are more similar than things in other clusters. The first step in creating clusters is to pick data points at random, ensuring that each one has an equal number of items. Improving K-means clustering's ability to handle a wide variety of data types and guarantee fair weight distribution, this research presents a new method for choosing the best cluster from uniform and non-uniform datasets.</span></p> |
| title | GENERALIZED K-MEANS CLUSTERING WITH CENTROID ENHANCEMENT: A STEP TOWARD ADAPTIVE DATA SEGMENTATION |
| topic | Clustering InitialCentroids k-means Algorithm |
| url | https://doi.org/10.5281/zenodo.16900105 |