HDDStream

Clustering of high dimensional data streams is an impor-tant problem in many application domains, a prominent example being network monitoring. Several approaches have been lately proposed for solving independently the dierent aspects of the problem. There exist methods for clustering over full dimensional streams and meth-ods for nding clusters in subspaces of high dimensional static data. Yet only a few approaches have been pro-posed so far which tackle both the stream and the high dimensionality aspects of the problem simultaneously. In this work, we propose a new density-based projected clustering algorithm, HDDStream, for high dimen-sional data streams. Our algorithm summarizes both the data points and the dimensions where these points are grouped together and maintains these summaries online, as new points arrive over time and old points ex-pire due to ageing. Our experimental results illustrate the eectiveness and the eciency of HDDStream and also demonstrate that it could serve as a trigger for de-tecting drastic changes in the underlying stream popu-lation, like bursts of network attacks.

StreamClustering
  6

Contributor

contributed at 2021-01-09

Authorship

Authorship is unclear, you can claim the item.

Classification(s)

Method-focused categoriesData-perspectiveGeoinformation analysis

Model Description

English {{currentDetailLanguage}} English

Below are quoted from: Ntoutsi, Irene, et al. "Density-based projected clustering over high dimensional data streams." Proceedings of the 2012 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, 2012.

Clustering of high dimensional data streams is an impor-tant problem in many application domains, a prominent example being network monitoring. Several approaches have been lately proposed for solving independently the dierent aspects of the problem. There exist methods for clustering over full dimensional streams and meth-ods for nding clusters in subspaces of high dimensional static data. Yet only a few approaches have been pro-posed so far which tackle both the stream and the high dimensionality aspects of the problem simultaneously. In this work, we propose a new density-based projected clustering algorithm, HDDStream, for high dimen-sional data streams. Our algorithm summarizes both the data points and the dimensions where these points are grouped together and maintains these summaries online, as new points arrive over time and old points ex-pire due to ageing. Our experimental results illustrate the eectiveness and the eciency of HDDStream and also demonstrate that it could serve as a trigger for de-tecting drastic changes in the underlying stream popu-lation, like bursts of network attacks.

Model Metadata

Name {{metadata.overview.name}}
Version {{metadata.overview.version}}
Model Type {{metadata.overview.modelType}}
Model Domain
{{domain}}
Sacle {{metadata.overview.scale}}

There is no overview about this model. You can click to add overview.

Purpose {{metadata.design.purpose}}
Principles
{{principle}}
Incorporated Models
{{incorporatedModel}}
Model part of larger framework: {{metadata.design.framework}}
Incorporated Models
{{process}}

There is no design info about this model. You can click to add overview.

Information {{metadata.usage.information}}
Initialization {{metadata.usage.initialization}}
Hardware Requirements {{metadata.usage.hardware}}
Software Requirements {{metadata.usage.software}}
Inputs
{{input}}
Outputs
{{output}}

There is no usage info about this model. You can click to add overview.

How to Cite

Copy

QR Code

Contributor(s)

Initial contribute: 2021-01-09

Authorship

Authorship is unclear, you can claim the item.

QR Code

×

{{curRelation.overview}}
{{curRelation.author.join('; ')}}
{{curRelation.journal}}









You can link related {{typeName}} from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

Related Items
Related Items

You can link resource from repository to this model item, or you can create a new {{typeName.toLowerCase()}}.

Drop the file here, orclick to upload.
Select From My Space
+ add

These authorship information will be submitted to the contributor to review.

Cancel Submit
Model Classifications
Cancel Submit
Localizations + Add
{{ item.label }} {{ item.value }}
Model Name :
Cancel Submit
Name:
Version:
Model Type:
Model Domain:
Scale:
Purpose:
Principles:
Incorporated models:

Model part of

larger framework

Process:
Information:
Initialization:
Hardware Requirements:
Software Requirements:
Inputs:
Outputs:
Cancel Submit
Title Author Date Journal Volume(Issue) Pages Links Doi Operation
Cancel Submit
Add Cancel

{{articleUploading.title}}

Authors:  {{articleUploading.authors[0]}}, {{articleUploading.authors[1]}}, {{articleUploading.authors[2]}}, et al.

Journal:   {{articleUploading.journal}}

Date:   {{articleUploading.date}}

Page range:   {{articleUploading.pageRange}}

Link:   {{articleUploading.link}}

DOI:   {{articleUploading.doi}}

Yes, this is it Cancel

The article {{articleUploading.title}} has been uploaded yet.

OK
Cancel Confirm