Methods to perform hierarchical agglomerative clustering using SciPy and parse the clusters.

hac_cluster.py

	def cluster(self):
		# ... #
		
		# cluster the codes together using hierarchical agglomerative
		# clustering and then parse the returned clusters
		self.clusters = hierarchy.linkage(clusterCodes, "single", "euclidean")
		self.clusters = self.parseClusters(self.clusters, numClusters)
		
		# ... #
	
	def parseClusters(self, clusters, numClusters):
		# initialize the parsed clusters dictionary and the current
		# cluster index
		parsed = {}
		j = len(self.index)

		# loop over the clusters
		for i in range(0, len(self.index) - numClusters):
			# grab the index of the two clusters that were merged
			# together
			idA = int(clusters[i][0])
			idB = int(clusters[i][1])
	
			# try to grab the set of nodes already in the cluster
			# for each ID
			nodesA = parsed.get(idA, set())
			nodesB = parsed.get(idB, set())
	
			# if the first node set is larger than zero, delete the
			# cluster ID
			if len(nodesA) > 0:
				del parsed[idA]
	
			# if the second node set is larger than zero, delete the
			# cluster ID
			if len(nodesB) > 0:
				del parsed[idB]

			# if the first ID is less than the length of the index, then
			# add the ID to the nodes
			if idA < len(self.index):
				nodesA.add(idA)
	
			# if the second ID is less than the length of the index, then
			# add the ID to the nodes
			if idB < len(self.index):
				nodesB.add(idB)
	
			# take the union of the set of nodes, update the parsed clusters
			# dictionary, and then increment the next available cluster ID
			nodesA = nodesA.union(nodesB)
			parsed[j] = nodesA
			j += 1

		# return the parsed clusters dictionary
		return parsed

Could anyone tell me please how can I perform Agglomerative clustering on an image??? I have got the slightest idea of this algo.