A Python package for unsupervised mix data types clustering
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Mixclu is a library for doing unsupervised mix data types clustering. This includes a variety of combination models including kmeans-onehot, gower distance, umap etc. The goal is to provide an easy to use implementation for each algorithm along with a scikit-learn API.
from mixclu import *
id_col, cat_columns, con_col = get_types(df)
""" define continuous and categorical columns"""
umap_kbins_emd, kbins_umap_model = categorical_embedding_model(df,
bin_con_columns = con_col,
no_of_clusters = 4,
bin_bins = 5)from mixclu import *
"""Kmeans one hot mix model"""
model_result = kmeans_onehot_mix(df,
cat_columns,
total_clusters = 4)
from mixclu import *
"""using gower distance as embeddings"""
clusters, gower_embeddings = kmoid_grower(df,
cat_columns,
no_of_clusters = 4)
from mixclu import *
"""(Mirkin, 2005, pp. 85–91)"""
z_score, model_result = Mirkin_model(df,
cat_columns,
no_of_clusters = 4)
- Autoencoder, Vae based clustering
- Tabformer, tabnet based embeddings then clustering
- Implementing paper : Affinity Learning for Mixed Data Clustering
- Implementing paper : A Multi-View Clustering for Mixed Data