{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Implements k-modes and k-prototypes clustering algorithms for categorical and mixed numerical/categorical data, with a scikit-learn-compatible interface.","skillfed_tags":["clustering","categorical-data","scikit-learn-compatible"],"use_cases":["Cluster customer records with mixed demographic (categorical) and behavioral (numerical) attributes.","Segment product categories or survey responses where most features are discrete or nominal.","Analyze categorical genomic or medical data where Euclidean distance is not meaningful.","Reduce dimensionality or find patterns in text-encoded or one-hot-encoded feature sets.","Benchmark or compare k-modes results against k-means on datasets with predominantly categorical features."],"what_it_does":"kmodes provides Python implementations of k-modes and k-prototypes clustering, specialized for categorical and mixed data types. Unlike k-means, which clusters numerical data by Euclidean distance, k-modes clusters categorical variables by counting matching categories between points. k-prototypes extends this to handle datasets with both numerical and categorical features. The library mirrors scikit-learn's clustering API, making it familiar to users of that ecosystem.\n\nThe package relies on numpy for computation and supports parallel execution via joblib for multiple initialization runs. It includes initialization strategies such as Huang's method and density-based approaches. All runtime dependencies (numpy, scikit-learn, scipy, joblib) are standard data-science libraries with low installation friction.","worth_installing":"Yes, if you need to cluster categorical or mixed-type data and want a scikit-learn-compatible interface. The low install friction and permissive license make it a straightforward choice. However, the dormant maintenance status (last release 2022-09-06) warrants checking compatibility with your current numpy and scikit-learn versions before committing to production use."},"id":"kmodes","links":{"html":"https://skillfed.io/packages/kmodes","md":"https://skillfed.io/packages/kmodes.md","pypi":"https://pypi.org/project/kmodes/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2022-09-06","license_spdx":null,"license_treatment":"permissive","name":"kmodes","python_support":"unspecified","summary":"Python implementations of the k-modes and k-prototypes clustering algorithms for clustering categorical data."},"popularity":{"monthly_downloads":561891,"position":5992,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.2"}