DenStream | Python implementation of the data stream clustering | Machine Learning library

by issamemari Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | DenStream Summary

DenStream is a Python library typically used in Artificial Intelligence, Machine Learning applications. DenStream has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However DenStream build file is not available. You can download it from GitHub.

This a Python implementation of the data stream clustering algorithm "DenStream". The implementation is compatible with scikit-learn and follows the scikit-learn API for clustering algorithms. Details about how the algorithm works can be found in the original paper "Density-Based Clustering over an Evolving Data Stream with Noise", which is available here.

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Security

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DenStream has a low active ecosystem.

It has 42 star(s) with 12 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 0 have been closed. On average issues are closed in 312 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DenStream is current.

Quality

DenStream has 0 bugs and 0 code smells.

Security

DenStream has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

DenStream code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DenStream is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

DenStream releases are not available. You will need to build from source code and install.

DenStream has no build file. You will be need to create the build yourself to build the component from source.

It has 340 lines of code, 23 functions and 4 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed DenStream and discovered the below as its top functions. This is intended to give you an instant insight into DenStream implemented functionality, and help decide if they suit your requirements.

Fit the predictive clustering
Insert a new sample
Attempt to merge the given sample into a new one
Performs partial fitting
Validate the sample weight
Find the nearest micro clusters in the given sample
Try to merge the given sample with the given weight
Calculate the decay function
Return sum of weights
Perform partial clustering
Calculate the radius
The radius of the distribution
Center of the mean
Center of the linear sum

Get all kandi verified functions for this library.

DenStream Key Features

No Key Features are available at this moment for DenStream.

DenStream Examples and Code Snippets

No Code Snippets are available at this moment for DenStream.

Community Discussions

Trending Discussions on DenStream

java.lang.NullPointerException when trying MOA stream clustering algorithm denstream.WithDBSCAN (How to properly use it?)

Better way to get R/Java (MOA) clustering algorithms results and process them with python

QUESTION

java.lang.NullPointerException when trying MOA stream clustering algorithm denstream.WithDBSCAN (How to properly use it?)

Asked 2019-Nov-19 at 07:04

I am new into using moa and I am having a hard time trying to decode how the clustering algorithms have to be used. The documentation lacks of sample code for common usages, and the implementation is not well explained with comments ... have not found any tutorial either.

So, here is my code:

...

ANSWER

Answered 2019-Nov-19 at 07:04

I have updated the code. It is working as I mentioned in the github, you have to assign header to your instance. See the github discussion

here is the updated code:

Source https://stackoverflow.com/questions/58869442

QUESTION

Better way to get R/Java (MOA) clustering algorithms results and process them with python

Asked 2019-Nov-13 at 16:31

I have always used Python for clustering, but recently I came across a situation in which I need the implementations of both CluStream and DenStream (stream clustering algorithms), available in R and Java (there are some implementations in Python from the community but I already tried them and they do no work).

The thing is that I have to compare many clustering algorithms written in Python, and as a prev stage I was using the well known scikit learn data sets (to show how algorithms handle non-globular clusters - of course then I will use time series data).

Now, I wanna know if the proper way to try those R/Java algorithms and compute a metric coded in Python (DBCV) with the R/Java clustering results ....

--> So, summing up, I need to compare many algorithms (coded in Python and R/Java) using the same data sets (which I figured could be persisted into csv files) and computing the same validity metric (Python).

Any help would be appreciated. Thanks in advance!

EDIT: the solution I came across is the following:

Generate the toy data sets with sklearn and persist them into csv files
Use the different clustering algorithms with those data sets and persist also the clustering results into csv files (it does not matter which programming language it's used)
Develop another app which:
- takes the clustering solutions stored in the cvs files
- computes the metric and shows the results

PLEASE let me know if you find a better solution!

Notes:

This R package is the one i wanna try: streamMOA
I do not know anything about R and I have worked with Java before (what implementation I choose depends on the better approach regarding the integration with Python)

...

ANSWER

Answered 2019-Nov-13 at 16:31

MOA is a Java software. There is no good reason to use it via R unless you are already in the R ecosystem (which you aren't).
You can write the data to CSV and load it in whatever tool you like
These data sets are not streams. They lack all the difficulties and challenges of streams - a simple subsample will be enough to identify the clustering structure. Conclusions drawn from this data are useless. Use real data streams, not synthetic data with no sequential order to it.

Source https://stackoverflow.com/questions/58769612

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DenStream

You can download it from GitHub.
You can use DenStream like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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