Clustering Analysis - Revision history

Ollvihe at 18:14, 17 October 2024

2024-10-17T18:14:15Z

← Older revision		Revision as of 18:14, 17 October 2024
Line 1:		Line 1:
	Clustering Analysis can be used to explain what kind of cases the eventlog contains. It divides cases into groups in a way that cases within each group are as similar as possible in terms of the case attribute values and occurred event types. Clustering is based on a unsupervised machine learning and uses the ''kmodes'' algorithm with categorized values for case attribute values and event type occurrences. Due to the nature of the algorithm, different clustering runs may end up to different results. Also, ~~the~~ clustering ~~is usually not performed for~~ all cases ~~but~~ a sample of cases ~~(to improve performance)~~ because when the sample is representative, it would provide as good results as with the entire dataset. See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering.		Clustering Analysis can be used to explain what kind of cases the eventlog contains. It divides cases into groups in a way that cases within each group are as similar as possible in terms of the case attribute values and occurred event types. Clustering is based on a unsupervised machine learning and uses the ''kmodes'' algorithm with categorized values for case attribute values and event type occurrences. Due to the nature of the algorithm, different clustering runs may end up to different results. Also, instead of performing clustering to all cases, a sample of cases is taken because when the sample is representative, it would provide as good results as with the entire dataset (but with improved performance). See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering.

	Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.		Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.
Line 23:		Line 23:
	* '''Events''': Event types that occurrences are taken to the clustering analysis. If none is selected, all event types are selected.		* '''Events''': Event types that occurrences are taken to the clustering analysis. If none is selected, all event types are selected.

	Also the size of the sample used in the clustering can be changed in the chart settings (in the ''Analyze'' tab).		Also the size of the sample used in the clustering can be changed in the chart settings (in the ''Analyze'' tab). The maximum allowed sample size is 10000 cases because samples greater than that wouldn't improve results but would slow down performance.

	== Clustering Analysis Calculation==		== Clustering Analysis Calculation==

Ollvihe at 22:55, 1 September 2023

2023-09-01T22:55:39Z

← Older revision		Revision as of 22:55, 1 September 2023
Line 3:		Line 3:
	Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.		Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.

	[[File:Clusteringanalysis.png\|~~1000px~~]]		[[File:Clusteringanalysis.png\|900px]]

	== Using Clustering Analysis ==		== Using Clustering Analysis ==

Ollvihe: /* Clustering Analysis Calculation */

2023-07-31T12:21:03Z

Clustering Analysis Calculation

← Older revision		Revision as of 12:21, 31 July 2023
Line 32:		Line 32:

	The root causes analysis is used so that the clustering analysis wouldn't need show the individual cases in each cluster, but the features that describe each cluster (a long list of individual cases wouldn't be very easy to read). Note that the case attribute and event type settings are used both in determining which features are taken to the clustering phase and also for which features the root causes analysis is run.		The root causes analysis is used so that the clustering analysis wouldn't need show the individual cases in each cluster, but the features that describe each cluster (a long list of individual cases wouldn't be very easy to read). Note that the case attribute and event type settings are used both in determining which features are taken to the clustering phase and also for which features the root causes analysis is run.

			Technically, the feature data used by the clustering analysis are numeric values between 0 and 1. Case attribute values are converted into that format as follows:
			* Numbers and dates are scaled between 0 and 1 (minimumum value is converted to 0 and maximum to 1)
			* Textual columns are "one-hot" encoded into multiple columns in a way that each unique value gets an own column. For each unique value column, when value is 1, the case has that particular attribute value and when value is 0, the attribute value is something else.

Ollvihe: /* Using Clustering Analysis */

2023-07-31T12:08:13Z

Using Clustering Analysis

← Older revision		Revision as of 12:08, 31 July 2023
Line 13:		Line 13:
	* '''Total density %''': Share of cases having this feature in the entire eventlog, i.e., the total number of cases having the value shown on the row divided by the total number of cases * 100.		* '''Total density %''': Share of cases having this feature in the entire eventlog, i.e., the total number of cases having the value shown on the row divided by the total number of cases * 100.
	* '''Contribution %''': Explains how much more common this feature is in this cluster when comparing to the entire eventlog. The higher the value, the more the feature characterizes this particular cluster. The contribution percentage is calculated as a subtraction of the cluster density and the total density percentages.		* '''Contribution %''': Explains how much more common this feature is in this cluster when comparing to the entire eventlog. The higher the value, the more the feature characterizes this particular cluster. The contribution percentage is calculated as a subtraction of the cluster density and the total density percentages.

			Note that in some special datasets, there may be less clusters returned than requested. This occurs for example when there are less cases than clusters requested, or the features of the cases don't have unique enough values.

	== Clustering Analysis Settings ==		== Clustering Analysis Settings ==

Ollvihe at 16:14, 30 July 2023

2023-07-30T16:14:53Z

← Older revision		Revision as of 16:14, 30 July 2023
Line 24:		Line 24:

	== Clustering Analysis Calculation==		== Clustering Analysis Calculation==
	Calculating the clustering analysis ~~has the following steps~~:		Calculating the clustering analysis is performed as follows:
	# Taking a random sample of cases from the eventlog.		# Taking a random sample of cases from the entire eventlog.
	# Performing the clustering ~~using machine learning~~ to the cases. ~~The~~ result ~~is that~~ each ~~cases~~ belongs to a certain cluster.		# Performing the clustering to the cases using the K-means algorithm. As a result, each case belongs to a certain cluster.
	# [[Root_Causes\|Root causes]] is run to find the ~~explaining factors~~ for each clusters.		# [[Root_Causes\|Root causes analysis]] is run to find the characterizing features for each clusters.

	The root causes analysis is used so that the clustering analysis ~~does not~~ show the individual cases in each cluster, but the features that describe each cluster (a long list of individual cases wouldn't be very easy to read). Note that the case attribute and event type settings ~~affect the data~~ features ~~that~~ are ~~given~~ to the clustering phase.		The root causes analysis is used so that the clustering analysis wouldn't need show the individual cases in each cluster, but the features that describe each cluster (a long list of individual cases wouldn't be very easy to read). Note that the case attribute and event type settings are used both in determining which features are taken to the clustering phase and also for which features the root causes analysis is run.

	~~[[Category: QPR ProcessAnalyzer]]~~

Ollvihe at 16:10, 30 July 2023

2023-07-30T16:10:15Z

← Older revision		Revision as of 16:10, 30 July 2023
Line 1:		Line 1:
	Clustering Analysis can be used to explain what kind of cases the eventlog contains. It divides cases into groups in a way that cases within each group are as similar as possible in terms of the case attribute values and occurred event types. Clustering is based on a unsupervised machine learning and uses the ''kmodes'' algorithm with categorized values for case attribute values and event type occurrences. Due to the nature of the algorithm, different clustering runs may end up to different results. See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering.		Clustering Analysis can be used to explain what kind of cases the eventlog contains. It divides cases into groups in a way that cases within each group are as similar as possible in terms of the case attribute values and occurred event types. Clustering is based on a unsupervised machine learning and uses the ''kmodes'' algorithm with categorized values for case attribute values and event type occurrences. Due to the nature of the algorithm, different clustering runs may end up to different results. Also, the clustering is usually not performed for all cases but a sample of cases (to improve performance) because when the sample is representative, it would provide as good results as with the entire dataset. See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering.

	Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.		Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.

Ollvihe at 16:07, 30 July 2023

2023-07-30T16:07:35Z

← Older revision		Revision as of 16:07, 30 July 2023
Line 1:		Line 1:
	Clustering Analysis divides cases into groups in a way that ~~the~~ cases within each group are as similar ~~to each other~~ as possible, in terms of the case attribute values and occurred event types. Clustering is based on a ~~so called~~ unsupervised machine learning ~~algorithm. The clustering analysis~~ uses the ''kmodes'' algorithm with categorized values for event type occurrences ~~and case attribute values~~. Due to the nature of the algorithm, different clustering runs may end up to ~~slightly~~ different results. See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering. Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.		Clustering Analysis can be used to explain what kind of cases the eventlog contains. It divides cases into groups in a way that cases within each group are as similar as possible in terms of the case attribute values and occurred event types. Clustering is based on a unsupervised machine learning and uses the ''kmodes'' algorithm with categorized values for case attribute values and event type occurrences. Due to the nature of the algorithm, different clustering runs may end up to different results. See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering.

			Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.

	[[File:Clusteringanalysis.png\|1000px]]		[[File:Clusteringanalysis.png\|1000px]]

	== Clustering Analysis ~~Overview~~ ==		== Using Clustering Analysis ==
	Clustering analysis is available as a view in the [[Navigation_Menu#Clustering_Analysis\|Navigation menu]]. ~~When~~ creating custom ~~dashboards~~, the clustering can ~~also~~ be opened as a preset to ~~add~~ it to the dashboard. The dashboard will remember the settings ~~made for the clustering~~.		Clustering analysis is available as a view in the [[Navigation_Menu#Clustering_Analysis\|Navigation menu]]. Also when creating a custom dashboard, the clustering can be opened as a preset to get it to the dashboard. The dashboard will remember the clustering settings if they are changed.


	~~The clustering~~ analysis is shown in a table where rows are grouped ~~as follows: Each~~ group is a cluster and each row shows describing features in the cluster. There are following columns:		Clustering analysis is shown in a table where rows are grouped in a way that each group is a cluster and each row shows describing features in the cluster. There are the following columns:
	* '''Feature''': The describing feature of the cluster, i.e., the case attribute and its value, or the event type name.		* '''Feature''': The describing feature of the cluster, i.e., the case attribute and its value, or the occurred event type name.
	* '''Cluster density %''': Share of cases having this feature ~~value~~ within the cluster, i.e. the number of cases having the value shown on the row in this particular cluster divided by the number of cases in the cluster * 100.		* '''Cluster density %''': Share of cases having this feature within the cluster, i.e. the number of cases having the value shown on the row in this particular cluster divided by the number of cases in the cluster * 100.
	* '''Total density %''': Share of cases having this feature in the entire eventlog, i.e., the total number of cases having the value shown on the row divided by the total number of cases * 100.		* '''Total density %''': Share of cases having this feature in the entire eventlog, i.e., the total number of cases having the value shown on the row divided by the total number of cases * 100.
	* '''Contribution %''': Explains how much more common this feature is in this cluster when comparing to the entire eventlog. The higher the value, the more the feature characterizes ~~the~~ cluster. The contribution percentage is calculated as a subtraction of the cluster density and total density percentages.		* '''Contribution %''': Explains how much more common this feature is in this cluster when comparing to the entire eventlog. The higher the value, the more the feature characterizes this particular cluster. The contribution percentage is calculated as a subtraction of the cluster density and the total density percentages.

	== Clustering Analysis Settings ==		== Clustering Analysis Settings ==

Ollvihe at 19:15, 28 July 2023

2023-07-28T19:15:49Z

← Older revision		Revision as of 19:15, 28 July 2023
Line 1:		Line 1:
	Clustering Analysis divides cases into groups in a way that the cases within each group are as similar to each other as possible, in terms of the case attribute values and occurred event types. Clustering is based on a so called unsupervised machine learning algorithm. The clustering analysis uses the ''kmodes'' algorithm with categorized values for event type occurrences and case attribute values. Due to the nature of the algorithm, different clustering runs may end up to slightly different results. See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering. Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.		Clustering Analysis divides cases into groups in a way that the cases within each group are as similar to each other as possible, in terms of the case attribute values and occurred event types. Clustering is based on a so called unsupervised machine learning algorithm. The clustering analysis uses the ''kmodes'' algorithm with categorized values for event type occurrences and case attribute values. Due to the nature of the algorithm, different clustering runs may end up to slightly different results. See this [https://en.wikipedia.org/wiki/Cluster_analysis Wikipedia article] for more about the idea behind clustering. Clustering analysis is an easy way to understand and explain the eventlog without knowing anything about it beforehand. It can also be used to check data integrity, as the analysis might reveal that the eventlog actually contains data from two distinct processes that cannot actually be compared to each other.

			[[File:Clusteringanalysis.png\|1000px]]

	== Clustering Analysis Overview ==		== Clustering Analysis Overview ==
	Clustering analysis is available as a view in the [[Navigation_Menu#Clustering_Analysis\|Navigation menu]]. When creating custom dashboards, the clustering can also be opened as a preset to add it to the dashboard. The dashboard will remember the settings made for the clustering.		Clustering analysis is available as a view in the [[Navigation_Menu#Clustering_Analysis\|Navigation menu]]. When creating custom dashboards, the clustering can also be opened as a preset to add it to the dashboard. The dashboard will remember the settings made for the clustering.

	~~[[File:Clusteringanalysis.png\|1100px]]~~

	The clustering analysis is shown in a table where rows are grouped as follows: Each group is a cluster and each row shows describing features in the cluster. There are following columns:		The clustering analysis is shown in a table where rows are grouped as follows: Each group is a cluster and each row shows describing features in the cluster. There are following columns:
Line 18:		Line 19:
	* '''Attributes''': Case attributes that are taken to the clustering analysis. If none is selected, all case attributes are selected. You can restrict which case attributes are selected, if you want the clustering to be done based on only certain features.		* '''Attributes''': Case attributes that are taken to the clustering analysis. If none is selected, all case attributes are selected. You can restrict which case attributes are selected, if you want the clustering to be done based on only certain features.
	* '''Events''': Event types that occurrences are taken to the clustering analysis. If none is selected, all event types are selected.		* '''Events''': Event types that occurrences are taken to the clustering analysis. If none is selected, all event types are selected.

			Also the size of the sample used in the clustering can be changed in the chart settings (in the ''Analyze'' tab).

	== Clustering Analysis Calculation==		== Clustering Analysis Calculation==

Ollvihe: /* Clustering Analysis Overview */

2023-07-28T19:09:17Z

Clustering Analysis Overview

← Older revision		Revision as of 19:09, 28 July 2023
Line 9:		Line 9:
	* '''Feature''': The describing feature of the cluster, i.e., the case attribute and its value, or the event type name.		* '''Feature''': The describing feature of the cluster, i.e., the case attribute and its value, or the event type name.
	* '''Cluster density %''': Share of cases having this feature value within the cluster, i.e. the number of cases having the value shown on the row in this particular cluster divided by the number of cases in the cluster * 100.		* '''Cluster density %''': Share of cases having this feature value within the cluster, i.e. the number of cases having the value shown on the row in this particular cluster divided by the number of cases in the cluster * 100.
	* '''Total density %''': Share of cases having this feature in the ~~whole~~ eventlog, i.e., the total number of cases having the value shown on the row divided by the total number of cases * 100.		* '''Total density %''': Share of cases having this feature in the entire eventlog, i.e., the total number of cases having the value shown on the row divided by the total number of cases * 100.
	* '''Contribution %''': ~~Number of cases that can be explained to belong to~~ this cluster ~~because of this feature value~~. The ~~scale is such that 0% means that~~ the feature ~~value isn't specific to this~~ cluster ~~and 100% means that all cases belonging to this cluster can be explained by this feature value~~. The contribution percentage is calculated as a subtraction of the cluster density and total density percentages.		* '''Contribution %''': Explains how much more common this feature is in this cluster when comparing to the entire eventlog. The higher the value, the more the feature characterizes the cluster. The contribution percentage is calculated as a subtraction of the cluster density and total density percentages.

	== Clustering Analysis Settings ==		== Clustering Analysis Settings ==

Ollvihe: /* Clustering Analysis Overview */

2023-07-28T19:07:15Z

Clustering Analysis Overview

← Older revision		Revision as of 19:07, 28 July 2023
Line 7:		Line 7:

	The clustering analysis is shown in a table where rows are grouped as follows: Each group is a cluster and each row shows describing features in the cluster. There are following columns:		The clustering analysis is shown in a table where rows are grouped as follows: Each group is a cluster and each row shows describing features in the cluster. There are following columns:
	* '''Feature~~''' and '''Value~~''': ~~These two columns list~~ the case attribute and ~~other values that are common to the cases in~~ the ~~cluster~~.		* '''Feature''': The describing feature of the cluster, i.e., the case attribute and its value, or the event type name.
	* '''Cluster ~~Density~~ %''': Share of cases having this feature value within the cluster (i.e. the number of cases having the value shown on the row in this particular cluster divided by the number of cases in the cluster * 100).		* '''Cluster density %''': Share of cases having this feature value within the cluster, i.e. the number of cases having the value shown on the row in this particular cluster divided by the number of cases in the cluster * 100.
	* '''Total ~~Density~~ %''': Share of cases having this feature ~~value~~ in the whole ~~data set (~~i.e. the total number of cases having the value shown on the row divided by the total number of cases * 100).		* '''Total density %''': Share of cases having this feature in the whole eventlog, i.e., the total number of cases having the value shown on the row divided by the total number of cases * 100.
	* '''Contribution %''': ~~Amount~~ of cases that can be explained to belong to this cluster because of this feature value. The scale is such that 0% means that the feature value isn't specific to this cluster and 100% means that all cases belonging to this cluster can be explained by this feature value.		* '''Contribution %''': Number of cases that can be explained to belong to this cluster because of this feature value. The scale is such that 0% means that the feature value isn't specific to this cluster and 100% means that all cases belonging to this cluster can be explained by this feature value. The contribution percentage is calculated as a subtraction of the cluster density and total density percentages.

	== Clustering Analysis Settings ==		== Clustering Analysis Settings ==