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1 | 1 | Applications |
2 | 2 | --------------- |
3 | 3 |
|
4 | | -One of the beauties of Mapper is that it can be used on wide variety of data and problems. Here we highlight a few cases. |
| 4 | +One of the beauties of Mapper is that it can be used on wide variety of data and |
| 5 | +problems. Here we highlight a few cases. |
5 | 6 |
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6 | 7 |
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7 | 8 | .. image:: http://i.imgur.com/N4YdyHS.png |
8 | 9 | :width: 300 px |
9 | 10 | :alt: Mapper of Wisconsin Breast Cancer Data |
10 | 11 | :align: right |
11 | 12 |
|
12 | | -Anomaly Detection |
| 13 | +Anomaly Detection |
13 | 14 | ====================== |
14 | 15 |
|
| 16 | +KeplerMapper can be used for anomaly detection. Anomaly detection is useful for |
| 17 | +fighting fraud and finding errors. Projecting with ``knn_distance_5`` can |
| 18 | +surface outliers. There are also specialized outlier detection algorithms, like |
| 19 | +the Isolation Forest and GLOSH, that make good projections for anomaly |
| 20 | +detection. |
15 | 21 |
|
| 22 | +Examples: |
16 | 23 |
|
17 | | -KeplerMapper can be used for anomaly detection. Anomaly detection is useful for fighting fraud and finding errors. Projecting with ``knn_distance_5`` can surface outliers. There are also specialized outlier detection algorithms, like the Isolation Forest and GLOSH, that make good projections for anomaly detection. |
18 | | - |
19 | | - - Notebooks: TDA Wisconsin Breast Cancer |
20 | | - - Demos: TDA Wisconsin Breast Cancer |
21 | | - |
22 | | - |
| 24 | +- Detecting breast cancer using the TDA Wisconsin dataset :doc:`[notebook] </notebooks/Cancer-demo>` |
| 25 | +- Detecting TOR network traffic :doc:`[notebook] </notebooks/TOR-XGB-TDA>` `[demo] <https://mlwave.github.io/tda/tor-tda.html>`_ |
23 | 26 |
|
24 | 27 | .. image:: https://i.imgur.com/CjUd2Of.png |
25 | 28 | :width: 300 px |
26 | 29 | :alt: Mapper of Word2Vec |
27 | 30 | :align: right |
28 | 31 |
|
| 32 | + |
29 | 33 | Natural Language Processing |
30 | 34 | =============================== |
31 | 35 |
|
32 | | -Mapper can be used to explore bias in Word2Vec models. |
| 36 | +Mapper can visualize the topography of categorized news articles. :doc:`[notebook] <notebooks/KeplerMapper-Newsgroup20-Pipeline>` |
33 | 37 |
|
34 | | - - Notebooks: Word Vector Gender Bias. |
35 | | - - Demos: Word Vector Gender Bias |
| 38 | +Mapper can be used to explore bias in Word2Vec models. |
| 39 | +`[demo] <http://mlwave.github.io/tda/word2vec-gender-bias.html>`__ |
36 | 40 |
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37 | 41 |
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38 | 42 |
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@@ -80,8 +84,4 @@ Uses & Mentions |
80 | 84 | topological data analysis for accurate prediction of manufacturing |
81 | 85 | system |
82 | 86 | outputs <https://www.researchgate.net/publication/314185934_Identification_of_Key_Features_Using_Topological_Data_Analysis_for_Accurate_Prediction_of_Manufacturing_System_Outputs>`__ |
83 | | -- **Christian Parsons** uses KeplerMapper in `Mapamundi <https://christian-parsons.com/mapamundi-wdvp/>`__: a world map based on a geometric interpretation of data, it takes socio-economic metrics for all countries and uses T-SNE as lens and DBSCAN as clusterer to make an alternative world map. Awarded on the `World Data Visualization Prize 2019 <https://informationisbeautiful.net/2019/winners-of-the-world-data-visualization-prize/>`__ |
84 | | - |
85 | | - |
86 | | - |
87 | | - |
| 87 | +- **Christian Parsons** uses KeplerMapper in `Mapamundi <https://christian-parsons.com/mapamundi-wdvp/>`__: a world map based on a geometric interpretation of data, it takes socio-economic metrics for all countries and uses T-SNE as lens and DBSCAN as clusterer to make an alternative world map. Awarded on the `World Data Visualization Prize 2019 <https://informationisbeautiful.net/2019/winners-of-the-world-data-visualization-prize/>`__ |
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