DBCV | Python implementation of Density-Based Clustering Validation | Machine Learning library

by christopherjenness Python Version: Current License: MIT

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

kandi X-RAY | DBCV Summary

DBCV is a Python library typically used in Artificial Intelligence, Machine Learning, Hadoop applications. DBCV has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

How do you validate clustering assignmnets from unsupervised learning algorithms? A common method is the Silhoette Method, which provides an objective score between -1 and 1 on the quality of clustering. The silhouette value measures how well an object is classified in its own cluster instead of neighboring clusters. The silhouette (and most other popular methods) work very well on globular clusters, but can fail on non-glubular clusters such as:.

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Support

DBCV has a low active ecosystem.

It has 102 star(s) with 35 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 13 open issues and 3 have been closed. On average issues are closed in 62 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DBCV is current.

Quality

DBCV has 0 bugs and 16 code smells.

Security

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

DBCV code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DBCV 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

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

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

DBCV saves you 97 person hours of effort in developing the same functionality from scratch.

It has 248 lines of code, 23 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Computes the DBCV
Construct a graph of the mutual reachability distance between points
Calculate the cluster validation index
Compute the mutual reachability distance between two points
Calculate the coredist of a point
Calculate the density separation separation between two clusters
Calculates the similarity index for each cluster
Calculates the density of the cluster density
Return the members of a given cluster
Compute the mutual reach distance from a distance tree
Generate sample data
Generates labels for k - means clustering

Get all kandi verified functions for this library.

DBCV Key Features

No Key Features are available at this moment for DBCV.

DBCV Examples and Code Snippets

No Code Snippets are available at this moment for DBCV.

Community Discussions

Trending Discussions on DBCV

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

API Gateway: ok on TEST button, 500 on Curl

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

QUESTION

API Gateway: ok on TEST button, 500 on Curl

Asked 2019-Jan-14 at 11:46

I checked all configurations and still cannot understand why it happens... My endpoint:

My successful Logs in TEST:

...

ANSWER

Answered 2019-Jan-13 at 18:02

Most of the time this is due to the 'Deploy API' issue. Did you forget to deploy after making changes?

You can also check in deployment history in those stages.

Enable Binary support for API Gateway: EDIT1:

The one part that is missing is enabling the passthrough for response. Checkout how to send binary response from a lambda or other services.

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-payload-encodings-configure-with-console.html

On the otherside you will have a problem when the size exceeds 6 MB. If the size is small then good.

Source https://stackoverflow.com/questions/54150912

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

Vulnerabilities

No vulnerabilities reported

Install DBCV

You can download it from GitHub.
You can use DBCV 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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