Metadata-Version: 2.1
Name: pqkmeans
Version: 1.0.6
Summary: Fast and memory-efficient clustering
Home-page: http://yusukematsui.me/project/pqkmeans/pqkmeans.html
Author: Keisuke Ogaki, Yusuke Matsui
Author-email: keisuke_ogaki@dwango.co.jp, matsui528@gmail.com
License: MIT License
Provides-Extra: texmex
License-File: LICENSE


PQk-means [Matsui, Ogaki, Yamasaki, and Aizawa, ACMMM 17] is a Python library for efficient clustering of large-scale data. By first compressing input vectors into short product-quantized (PQ) codes, PQk-means achieves fast and memory-efficient clustering, even for high-dimensional vectors. Similar to k-means, PQk-means repeats the assignment and update steps, both of which can be performed in the PQ-code domain.
For a comparison, we provide the ITQ encoding for the binary conversion and Binary k-means [Gong+, CVPR 15] for the clustering of binary codes.
The library is written in C++ for the main algorithm with wrappers for Python. All encoding/clustering codes are compatible with scikit-learn.
    
