https://github.com/xuyxu/clustering

Clustering / Subspace Clustering Algorithms on MATLAB
https://github.com/xuyxu/clustering

clustering clustering-algorithm subspace-clustering subspace-kmeans

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Clustering / Subspace Clustering Algorithms on MATLAB

• Host: GitHub
• URL: https://github.com/xuyxu/clustering
• Owner: xuyxu
• Created: 2017-01-07T08:13:44.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2020-10-28T11:21:08.000Z (about 4 years ago)
• Last Synced: 2024-11-07T19:17:49.128Z (7 days ago)
• Topics: clustering, clustering-algorithm, subspace-clustering, subspace-kmeans
• Language: MATLAB
• Homepage:
• Size: 36.1 KB
• Stars: 222
• Watchers: 12
• Forks: 88
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Clustering/Subspace Clustering Algorithms on MATLAB

**This repo is no longer in active development. However, any problem on implementations of existing algorithms is welcomed. [Oct, 2020]**

## 1. Clustering Algorithms

- **K-means**

- **K-means++**

    - Generally speaking, this algorithm is similar to **K-means**;

    - Unlike classic K-means randomly choosing initial centroids, a better initialization procedure is integrated into **K-means++**, where observations far from existing centroids have higher probabilities of being chosen as the next centroid.

    - The initializeation procedure can be achieved using Fitness Proportionate Selection.

- **ISODATA (Iterative Self-Organizing Data Analysis)**

    - To be brief, **ISODATA** introduces two additional operations: Splitting and Merging;

    - When the number of observations within one class is less than one pre-defined threshold, **ISODATA** merges two classes with minimum between-class distance; 

    - When the within-class variance of one class exceeds one pre-defined threshold, **ISODATA** splits this class into two different sub-classes.

- **Mean Shift**

	- For each point *x*, find neighbors, calculate mean vector *m*, update *x = m*, until *x == m*;

	- Non-parametric model, no need to specify the number of classes;

	- No structure priori.

- **DBSCAN (Density-Based Spatial Clustering of Application with Noise)**

	- Starting with pre-selected core objects, DBSCAN extends each cluster based on the connectivity between data points;

	- DBSCAN takes noisy data into consideration, hence robust to outliers;

	- Choosing good parameters can be hard without prior knowledge;

- **Gaussian Mixture Model (GMM)**

- **LVQ (Learning Vector Quantization)**

## 2. Subspace Clustering Algorithms

- **Subspace K-means**

    - This algorithm directly extends **K-means** to Subspace Clustering through multiplying each dimension *dj* by one weight *mj* (s.t. sum(*mj*)=1, *j*=1,2,...,*p*);

    - It can be efficiently sovled in an Expectation-Maximization (EM) fashion. In each E-step, it updates weights, centroids using Lagrange Multiplier;

    - This rough algorithm suffers from the problem on its favor of using just a few dimensions when clustering sparse data;

- **Entropy-Weighting Subspace K-means**

    - Generally speaking, this algorithm is similar to **Subspace K-means**;

    - In addition, it introduces one regularization item related to weight entropy into the objective function, in order to mitigate the aforementioned problem in **Subspace K-means**.

    - Apart from its succinctness and efficiency, it works well on a broad range of real-world datasets.