<?xml version='1.0' encoding='utf-8'?>
<scheme description="Hierarchical Clustering helps us find groups of
similar documents.&#10;&#10;First, we need to define what we mean by similar.
Distances computes cosine distance between documents based on word count
provided in Bag of Words. Then, Hierarchical Clustering plots a dendrogram - a
 hierarchical structure of documents by their similarity."
title="Hierarchical Clustering on Text" version="2.0">
	<nodes>
		<node id="0" name="Corpus" position="(70.0, 152.0)" project_name="Orange3-Text" qualified_name="orangecontrib.text.widgets.owcorpus.OWCorpus" title="Corpus" version="" />
		<node id="1" name="Preprocess Text" position="(179.0, 152.0)" project_name="Orange3-Text" qualified_name="orangecontrib.text.widgets.owpreprocess.OWPreprocess" title="Preprocess Text" version="" />
		<node id="2" name="Bag of Words" position="(292.0, 152.0)" project_name="Orange3-Text" qualified_name="orangecontrib.text.widgets.owbagofwords.OWTBagOfWords" title="Bag of Words" version="" />
		<node id="3" name="Distances" position="(405.0, 152.0)" project_name="Orange3" qualified_name="Orange.widgets.unsupervised.owdistances.OWDistances" title="Distances" version="" />
		<node id="4" name="Hierarchical Clustering" position="(567.0, 69.0)" project_name="Orange3" qualified_name="Orange.widgets.unsupervised.owhierarchicalclustering.OWHierarchicalClustering" title="Hierarchical Clustering" version="" />
		<node id="5" name="MDS" position="(570.0, 225.0)" project_name="Orange3" qualified_name="Orange.widgets.unsupervised.owmds.OWMDS" title="MDS" version="" />
		<node id="6" name="Corpus Viewer" position="(745.0, 68.0)" project_name="Orange3-Text" qualified_name="orangecontrib.text.widgets.owcorpusviewer.OWCorpusViewer" title="Corpus Viewer" version="" />
	</nodes>
	<links>
		<link enabled="true" id="0" sink_channel="Corpus" sink_node_id="1" source_channel="Corpus" source_node_id="0" />
		<link enabled="true" id="1" sink_channel="Corpus" sink_node_id="2" source_channel="Corpus" source_node_id="1" />
		<link enabled="true" id="2" sink_channel="Data" sink_node_id="3" source_channel="Corpus" source_node_id="2" />
		<link enabled="true" id="3" sink_channel="Distances" sink_node_id="4" source_channel="Distances" source_node_id="3" />
		<link enabled="true" id="4" sink_channel="Distances" sink_node_id="5" source_channel="Distances" source_node_id="3" />
		<link enabled="true" id="5" sink_channel="Data" sink_node_id="6" source_channel="Selected Data" source_node_id="4" />
	</links>
	<annotations>
		<arrow end="(98.0, 211.0)" fill="#C1272D" id="0" start="(137.0, 293.0)" />
		<text font-family="Helvetica" font-size="16" id="1" rect="(82.0, 303.0, 150.0, 44.0)" type="text/plain">Load 'grimm-tales-selected.tab'.</text>
		<arrow end="(396.0, 212.0)" fill="#C1272D" id="2" start="(382.00000000000006, 290.0)" />
		<text font-family="Helvetica" font-size="16" id="3" rect="(263.0, 297.0, 193.0, 112.0)" type="text/plain">Compute distances between documents with cosine distance. The smaller the distance, the more similar two document are.</text>
		<arrow end="(608.0, 260.0)" fill="#C1272D" id="4" start="(670.0, 313.0)" />
		<text font-family="Helvetica" font-size="16" id="5" rect="(670.0, 312.0, 208.0, 116.0)" type="text/plain">Plot the data in 2D. MDS projects multidimensional data, such as BoW, to a plane where similar documents lie closer together.</text>
		<arrow end="(609.0, 109.0)" fill="#C1272D" id="6" start="(685.0, 183.0)" />
		<text font-family="Helvetica" font-size="16" id="7" rect="(691.0, 179.0, 150.0, 77.0)" type="text/plain">Observe the hierarchy of the documents in a dendrogram.</text>
		<arrow end="(786.0, 77.0)" fill="#39B54A" id="8" start="(870.0, 135.0)" />
		<text font-family="Helvetica" font-size="16" id="9" rect="(885.0, 110.0, 150.0, 108.0)" type="text/plain">Select a cluster in Hierarchical Clustering and observe selected documents in Corpus Viewer.</text>
	</annotations>
	<thumbnail />
	<node_properties>
		<properties format="pickle" node_id="0">gAN9cQAoWBMAAABzYXZlZFdpZGdldEdlb21ldHJ5cQFDMgHZ0MsAAgAAAAAAAAAAABcAAAI3AAAB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</properties>
		<properties format="literal" node_id="1">{'filters': {'keep_n': 100, 'max_df': 0.9, 'enabled': True, 'use_df': False, 'pattern': '\\.|,|:|;|!|\\?|\\(|\\)|\\||\\+|\'|"|‘|’|“|”|\'|\\’|…|\\-|–|—|\\$|&amp;|\\*|&gt;|&lt;', 'min_df': 0.1, 'recent_lexicon_files': ['(none)'], 'recent_sw_files': ['(none)'], 'use_keep_n': False, 'stopwords_language': 'English', 'checked': [0]}, 'savedWidgetGeometry': None, 'tokenizer': {'pattern': '\\w+', 'enabled': True, 'method_index': 3}, '__version__': 1, 'transformers': {'checked': [0], 'enabled': True}, 'ngrams_range': {'ngrams_range': (1, 2), 'enabled': False}, 'normalizer': {'snowball_language': 'English', 'enabled': False, 'method_index': 0}, 'pos_tagger': {'stanford': {'recent_files': ['(none)'], 'resource_path': ''}, 'enabled': False, 'method_index': 0}, 'autocommit': True}</properties>
		<properties format="literal" node_id="2">{'savedWidgetGeometry': None, 'autocommit': True, 'normalization': '(None)', '__version__': 1, 'wlocal': 'Count', 'wglobal': '(None)'}</properties>
		<properties format="literal" node_id="3">{'savedWidgetGeometry': None, '__version__': 1, 'autocommit': True, 'axis': 0, 'metric_idx': 0}</properties>
		<properties format="pickle" node_id="4">gAN9cQAoWAsAAAB6b29tX2ZhY3RvcnEBSwBYBwAAAHBydW5pbmdxAksAWAkAAABjdXRfcmF0aW9x
[encoded data, 618537 characters]
</properties>
		<properties format="pickle" node_id="5">gAN9cQAoWBMAAABzYXZlZFdpZGdldEdlb21ldHJ5cQFOWAYAAABqaXR0ZXJxAksAWBMAAABsYWJl
[encoded data, 618460 characters]
</properties>
		<properties format="literal" node_id="6">{'show_tokens': False, 'savedWidgetGeometry': None, 'autocommit': True, 'context_settings': [], '__version__': 1}</properties>
	</node_properties>
</scheme>
