Consensus clustering for categorizing orthogonal vehicle operations - Revision history

Slawek at 17:56, 14 October 2018

2018-10-14T17:56:33Z

Mohbou at 16:41, 1 December 2017

2017-12-01T16:41:03Z

Mohbou at 13:13, 17 November 2017

2017-11-17T13:13:14Z

Mohbou at 14:24, 27 September 2017

2017-09-27T14:24:19Z

Mohbou at 12:36, 27 September 2017

2017-09-27T12:36:10Z

Mohbou at 12:33, 27 September 2017

2017-09-27T12:33:26Z

Mohbou at 11:23, 27 September 2017

2017-09-27T11:23:37Z

Mohbou at 11:21, 27 September 2017

2017-09-27T11:21:52Z

Mohbou: Consensus clustering for discovering orthogonal vehicle operations

2017-09-27T11:18:36Z

Consensus clustering for discovering orthogonal vehicle operations

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{{StudentProjectTemplate
|Summary=Consensus clustering for categorizing orthogonal vehicle operations
|Keywords=Data Mining, Machine Learning, Clustering, Unsupervised learning, knowledge discovery
|References=- Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4.

- Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210).

- Cui, Y., Fern, X. Z., & Dy, J. G. (2007). Non-redundant multi-view clustering via orthogonalization. In IEEE International Conference on Data Mining (ICDM), (pp. 133-142).

- Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.
|Prerequisites=Data mining course.
|Supervisor=Mohamed-Rafik Bouguelia, Sławomir Nowaczyk
|Level=Master
|Status=Open
}}
With the rapid development and growth of interconnected devices, more physical systems are integrated with computer-based systems, e.g. sensors and actuators can be sensed and controlled remotely across the network. It is enticing to analyze on-board sensor data streaming from devices (e.g. vehicle speed, engine torque etc.), in order to discover interesting patterns and knowledge. We have collected such data from Volvo buses in normal operation. It is interesting to analyze this data from the usage point of view, in order to discover and categorize various vehicle operations in an unsupervised way (using clustering).

Clustering is the task of grouping data in such a way that objects in the same group (i.e. cluster) are more similar to each other than to those in other groups (i.e. other clusters). Typical clustering algorithms output a single clustering (i.e. grouping) of the data. However, in real world applications (such as vehicle operation analysis), data can be interpreted in many different ways, leading to different groupings that are reasonable and interesting from different perspectives.

The goal of the thesis is to propose a method that allows to discover multiple clustering solutions, compare them, and find out if there is a single best (consensus) clustering, or multiple consistent clustering solutions. In the latter case, each data object would be grouped in multiple clusters, representing different perspectives on the data. (i.e. orthogonal clusters).

Clustering solutions that differ in a significant but consistent way can be obtained by constructing different views of the data, for example:
- Using different combinations of feature may reveal different structures of the data.
- Using different similarity/distance measures.
- Varying the hyperparameters of the clustering algorithm.
- Combining various clustering algorithms, etc.

While the main application focuses on grouping vehicle operations, the proposed method could be general and applicable for any data with such orthogonal clusters.

More details to come ....

Some initial references:

- Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4.
- Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210).
- Cui, Y., Fern, X. Z., & Dy, J. G. (2007). Non-redundant multi-view clustering via orthogonalization. In IEEE International Conference on Data Mining (ICDM), (pp. 133-142).
- Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.

@@ Line 2: / Line 2: @@
 |Summary=Discovering multiple clustering solutions, compare them, and find out if there is a single best (consensus) clustering, or multiple consistent clustering solutions.
 |Keywords=Data Mining, Machine Learning, Clustering, Unsupervised learning, knowledge discovery
-|References=- Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4.
+|References=- Some slides: https://www.siam.org/meetings/sdm11/clustering.pdf
 - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210).
@@ Line 29: / Line 41: @@
 While the main application focuses on grouping vehicle operations, the proposed method could be general and applicable for any data with such orthogonal clusters.
-More details to come ....
+References: check the of references above.
 Contact:

← Older revision		Revision as of 12:36, 27 September 2017
Line 39:		Line 39:

	- Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.		- Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.
		+
		+	Contact:
		+
		+	- Mohamed-Rafik Bouguelia ( mohbou@hh.se )
		+
		+	- Slawomir Nowaczyk ( slawomir.nowaczyk@hh.se )

@@ Line 23: / Line 23: @@
 |Prerequisites=Data mining course.
 |Supervisor=Mohamed-Rafik Bouguelia, Sławomir Nowaczyk
 |Level=Master
-|Status=Open
+|Status=Finished
 }}
 With the rapid development and growth of interconnected devices, more physical systems are integrated with computer-based systems, e.g. sensors and actuators can be sensed and controlled remotely across the network. It is enticing to analyze on-board sensor data streaming from devices (e.g. vehicle speed, engine torque etc.), in order to discover interesting patterns and knowledge. We have collected such data from Volvo buses in normal operation. It is interesting to analyze this data from the usage point of view, in order to discover and categorize various vehicle operations in an unsupervised way (using clustering).

@@ Line 30: / Line 30: @@
 Clustering is the task of grouping data in such a way that objects in the same group (i.e. cluster) are more similar to each other than to those in other groups (i.e. other clusters). Typical clustering algorithms output a single clustering (i.e. grouping) of the data. However, in real world applications (such as vehicle operation analysis), data can be interpreted in many different ways, leading to different groupings that are reasonable and interesting from different perspectives.
-The goal of the thesis is to propose a method that allows to discover multiple clustering solutions, compare them, and find out if there is a single best (consensus) clustering, or multiple consistent clustering solutions. In the latter case, each data object would be grouped in multiple clusters, representing different perspectives on the data. (i.e. orthogonal clusters).
+The goal of the thesis is to propose a method that allows to discover multiple clustering solutions, compare them, and find out if there is a single best (consensus) clustering, or multiple consistent clustering solutions. In the latter case, each data object would be grouped in multiple clusters, representing different perspectives on the data. (i.e. orthogonal, or independent clusterings).
 Clustering solutions that differ in a significant but consistent way can be obtained by constructing different views of the data, for example:

@@ Line 23: / Line 23: @@
 - Using different combinations of feature may reveal different structures of the data.
 - Using different similarity/distance measures.
 - Varying the hyperparameters of the clustering algorithm.
 - Combining various clustering algorithms, etc.

@@ Line 33: / Line 33: @@
 - Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4.
 - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210).
 - Cui, Y., Fern, X. Z., & Dy, J. G. (2007). Non-redundant multi-view clustering via orthogonalization. In IEEE International Conference on  Data Mining (ICDM), (pp. 133-142).
 - Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.

@@ Line 1: / Line 1: @@
 {{StudentProjectTemplate
-|Summary=Consensus clustering for categorizing orthogonal vehicle operations
+|Summary=The goal of the thesis is to propose a method that allows to discover multiple clustering solutions, compare them, and find out if there is a single best (consensus) clustering, or multiple consistent clustering solutions.
 |Keywords=Data Mining, Machine Learning, Clustering, Unsupervised learning, knowledge discovery
 |References=- Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4.