Skip to main content

db-robust-clust

In the era of big data, data scientists are trying to solve real-world problems using multivariate and heterogeneous datasets, i.e., datasets where for each unit multiple variables of different nature are observed. Clustering may be a challenging problem when data are of mixed-type and present an underlying correlation structure and outlying units.

In the paper Grané, A., Scielzo-Ortiz, F.: New distance-based clustering algorithms for large mixed-type data, Submitted to Journal of Classification (2025), new efficient robust clustering algorithms able to deal with large mixed-type data are developed and implemented in a new Python package, called db-robust-clust, hosted in the official Python Package Index (PyPI), the standard repository of packages for the Python programming language:: https://pypi.org/project/db_robust_clust/.

Their performance is analyzed in rather complex mixed-type datasets, both synthetic and real, where a wide variety of scenarios is considered regarding size, the proportion of outlying units, the underlying correlation structure, and the cluster pattern. The simulation study comprises four computational experiments conducted on datasets of sizes ranging from 35k to 1M, in which the accuracy and efficiency of the new proposals are tested and compared to those of existing clus- tering alternatives. In addition, the goodness and computing time of the methods under evaluation are tested on real datasets of varying sizes and patterns. MDS is used to visualize clustering results.

Metadata

Release files for db-robust-clust 0.1.14

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for db-robust-clust 0.1.14
File Size Uploaded
db_robust_clust-0.1.14.tar.gz 12.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for db-robust-clust 0.1.14
File Interpreter ABI Platform
db_robust_clust-0.1.14-py3-none-any.whl Python 3 none any Details

Total release size: 27.6 kB

Release files / db_robust_clust-0.1.14.tar.gz

Download URL db_robust_clust-0.1.14.tar.gz
Size 12.1 kB
Tags Source
SHA-256 checksum
How to use checksums
91221208f0fe8fe677b884ab6d252782fbabbf3833f4fb0a51363edbdb65183b
BLAKE2b-256 checksum
How to use checksums
1962a3a19bf13d56cbc68d4c7f3026ddde049b8eabff6413517ab84b19652d39
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.12.10

Release files / db_robust_clust-0.1.14-py3-none-any.whl

Download URL db_robust_clust-0.1.14-py3-none-any.whl
Size 15.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
6f5545165c53075d01353ff4d20f60b8207d60593ea8dd15e9ffc3b24dbc7def
BLAKE2b-256 checksum
How to use checksums
d431dabf778eb30a312827bafc82b6b7a6df28048f29f1b118507d2a3cd24f84
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.12.10

Release history Release notifications | RSS feed

This release

0.1.14 This release

2 release files

0.1.13

2 release files

0.1.10

2 release files

0.1.9

2 release files

0.1.8

2 release files

0.1.7

2 release files

0.1.6

2 release files

0.1.5

2 release files

0.1.4

2 release files

0.1.3

2 release files

0.1.2

2 release files

0.1.1

2 release files

0.1.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page