Add document clusterer

DOCUMENT_CLUSTERER_IMPLEMENTATION.md

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+# Document Clusterer Implementation Summary
+
+## Overview
+I've created a complete document clustering solution for the Natural library using K-Means algorithm combined with TF-IDF vectorization. This allows you to group similar documents together automatically.
+
+## Files Created
+
+### Core Implementation
+1. **`lib/natural/classifiers/kmeans.js`**
+   - K-Means clustering algorithm implementation
+   - Supports k-means++ and random initialization
+   - Configurable convergence parameters
+   - Methods: fit, predict, getCentroids, getAssignments, getClusters
+
+2. **`lib/natural/classifiers/document_clusterer.js`**
+   - High-level document clustering interface
+   - Integrates TF-IDF vectorization with K-Means
+   - Automatic vocabulary building and document vectorization
+   - Methods: addDocument, cluster, getClusterAssignments, getClusters, getClusterTerms, predict, getStatistics
+   - Serialization support (toJSON/fromJSON)
+
+### TypeScript Definitions
+3. **`lib/natural/classifiers/kmeans.d.ts`**
+   - Type definitions for KMeans class
+   - Interfaces: KMeansOptions
+
+4. **`lib/natural/classifiers/document_clusterer.d.ts`**
+   - Type definitions for DocumentClusterer class
+   - Interfaces: DocumentClustererOptions, ClusteredDocument, ClusterTerm, ClusteringStatistics
+
+### Tests
+5. **`spec/kmeans_spec.ts`**
+   - Comprehensive tests for KMeans algorithm
+   - Tests: basic clustering, initialization methods, convergence, prediction, error handling, high-dimensional data
+
+6. **`spec/document_clusterer_spec.ts`**
+   - Comprehensive tests for DocumentClusterer
+   - Tests: document clustering, prediction, cluster terms, statistics, serialization, options, error handling
+
+### Examples & Documentation
+7. **`examples/clustering/document_clustering_example.js`**
+   - Complete working example showing all features
+   - Demonstrates clustering, prediction, analysis, and serialization
+
+8. **`examples/clustering/README.md`**
+   - Comprehensive documentation
+   - API reference, examples, tips for best results
+
+### Updates to Existing Files
+9. **`lib/natural/classifiers/index.js`**
+   - Added exports for KMeans and DocumentClusterer
+
+10. **`lib/natural/classifiers/index.d.ts`**
+    - Added type exports for new classes
+
+## Quick Start
+
+```javascript
+const { DocumentClusterer } = require('natural');
+
+// Create clusterer with 3 clusters
+const clusterer = new DocumentClusterer(3);
+
+// Add documents
+clusterer.addDocument('The cat sat on the mat');
+clusterer.addDocument('Dogs are loyal pets');
+clusterer.addDocument('JavaScript is a programming language');
+clusterer.addDocument('Python is great for data science');
+
+// Cluster the documents
+clusterer.cluster();
+
+// View results
+const clusters = clusterer.getClusters();
+const clusterTerms = clusterer.getClusterTerms(5);
+
+// Predict cluster for new document
+const cluster = clusterer.predict('I love programming in Python');
+```
+
+## Features
+
+### K-Means Algorithm
+- ✅ Standard K-Means implementation  
+- ✅ K-means++ initialization for better results
+- ✅ Configurable convergence criteria
+- ✅ Support for any dimensionality
+- ✅ Prediction for new data points
+
+### Document Clusterer
+- ✅ Automatic TF-IDF vectorization
+- ✅ Vocabulary filtering (min frequency, max features)
+- ✅ Cluster analysis (top terms per cluster)
+- ✅ Statistics (cluster sizes, iterations, etc.)
+- ✅ Prediction for new documents
+- ✅ Serialization/deserialization
+- ✅ Integration with existing TfIdf class
+
+## Testing
+
+The implementation includes comprehensive test suites:
+- **KMeans**: 30+ test cases covering all functionality
+- **DocumentClusterer**: 25+ test cases covering all features
+
+## Example Output
+
+When you run the example (`node examples/clustering/document_clustering_example.js`):
+
+```
+Creating Document Clusterer with k=3 clusters...
+
+Adding documents...
+Clustering documents...
+
+Clustering Statistics:
+---------------------
+Number of clusters: 3
+Number of documents: 12
+Vocabulary size: 75
+Iterations: 2
+Cluster sizes: 10, 1, 1
+
+Documents by Cluster:
+--------------------
+
+Cluster 0 (10 documents):
+  - [doc_0]: The basketball game was exciting...
+  - [doc_1]: Soccer match ended in a 2-1 victory...
+  ...
+
+Top Terms by Cluster:
+--------------------
+
+Cluster 0:
+  final: 0.4773
+  team: 0.4773
+  92: 0.2792
+  ...
+
+Predicting clusters for new documents:
+--------------------------------------
+
+"The hockey team won the playoff game"
+  -> Cluster 0
+...
+```
+
+## Use Cases
+
+1. **Content Organization**: Automatically group similar articles, documents, or posts
+2. **Topic Discovery**: Identify common themes in large document collections
+3. **Search Enhancement**: Improve search by clustering related content
+4. **Data Analysis**: Explore and understand text datasets
+5. **Recommendation Systems**: Group similar content for recommendations
+
+## Next Steps
+
+To run the tests:
+```bash
+npm run build:tests
+npm test -- --testNamePattern="KMeans|DocumentClusterer"
+```
+
+To use in your project:
+```javascript
+const natural = require('natural');
+const { DocumentClusterer, KMeans } = natural;
+```
+
+## Notes
+
+- The implementation does NOT use any external K-Means library - it's a custom implementation
+- It integrates seamlessly with Natural's existing TF-IDF implementation
+- The API design follows Natural's conventions for classifiers
+- Full TypeScript support with type definitions
+- Comprehensive error handling and validation