Update README.md

README.md

@@ -1,6 +1,6 @@
 # HNSW4Hashes
 
-HNSW4Hashes is a specialized implementation of the HNSW (Hierarchical Navigable Small World) data structured adapted for efficient nearest neighbor search of fuzzy hashes. 
+HNSW4Hashes is a specialized implementation of the HNSW (Hierarchical Navigable Small World) data structured adapted for efficient nearest neighbor search of approximate matching hashes. 
 
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 
@@ -29,7 +29,7 @@ from tlsh_algorithm import TLSHHashAlgorithm
 # Create an HNSW structure
 myHNSW = HNSW(M=4, ef=4, Mmax=8, Mmax0=16)
 
-# Create the nodes based on TLSH Fuzzy Hashes
+# Create the nodes based on TLSH approximate matching hashes
 node1 = HashNode("T12B81E2134758C0E3CA097B381202C62AC793B46686CD9E2E8F9190EC89C537B5E7AF4C", TLSHHashAlgorithm)
 node2 = HashNode("T10381E956C26225F2DAD9D5C2C5C1A337FAF3708A25012B8A1EACDAC00B37D557E0E714", TLSHHashAlgorithm)
 node3 = HashNode("T1DF8174A9C2A506F9C6FFC292D6816333FEF1B845C419121A0F91CF5359B5B21FA3A304", TLSHHashAlgorithm)
@@ -39,7 +39,7 @@ myHNSW.add_node(node1)
 myHNSW.add_node(node2)
 myHNSW.add_node(node3)
 
-# Perform k-nearest neighbor search based on TLSH fuzzy hash similarity
+# Perform k-nearest neighbor search based on TLSH approximate matching hashes
 query_node = myHNSW.add_node(HashNode("T1BF81A292E336D1F68224D4A4C751A2B3BB353CA9C2103BA69FA4C7908761B50F22E301", TLSHHashAlgorithm))
 results = myHNSW.knn_search(query_node, k=5)
 
@@ -54,4 +54,4 @@ myHNSW = HNSW.load("myHNSW.txt")
 ```
 
 ## License
-GNU General Public License v3.0
+GNU General Public License v3.0