معرفی شرکت ها
estream-0.0.3
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
An E-Stream implementation in Python
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | estream-0.0.3 |
| نام | estream |
| نسخه کتابخانه | 0.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Chanon Jenakom |
| ایمیل نویسنده | chanonjenakom@gmail.com |
| آدرس صفحه اصلی | https://github.com/mickeycj/estream |
| آدرس اینترنتی | https://pypi.org/project/estream/ |
| مجوز | - |
.. image:: https://img.shields.io/pypi/v/estream.svg
:target: https://pypi.python.org/pypi/estream
:alt: PyPI Version
.. image:: https://img.shields.io/pypi/l/estream.svg
:target: https://github.com/mickeycj/estream/blob/master/LICENSE
:alt: License
.. image:: https://travis-ci.org/mickeycj/estream.svg
:target: https://travis-ci.org/mickeycj/estream
:alt: Travis CI Build Status
====================================
An E-Stream implementation in Python
====================================
E-Stream is an evolution-based technique for stream clustering which supports
five behaviors:
1. Appearance
2. Disappearance
3. Self-evolution
4. Merge
5. Split
These behaviors are achieved by representing each cluster as a *Fading Cluster
Structure with Histogram (FCH)*, utilizing a histogram for each feature of the
data.
The details for the underlying concepts can be found `here <https://www.researchgate.net/publication/221571035_E-Stream_Evolution-Based_Technique_for_Stream_Clustering>`_:
Udommanetanakit, K, Rakthanmanon, T, Waiyamai, K, *E-Stream: Evolution-Based
Technique for Stream Clustering*, Advanced Data Mining and Applications: Third
International Conference, 2007
-------------------
How to use E-Stream
-------------------
The ``estream`` package aims to be substibutable with ``sklearn`` classes so it
can be used interchangably with other transformers with similar API.
.. code-block:: python
from estream import EStream
from sklearn.datasets.samples_generator import make_blobs
estream = EStream()
data, _ = make_blobs()
estream.fit(data)
E-Stream contains a number of parameters that can be set; the major ones are as
follows:
- ``max_clusters``: This limits the number of clusters the clustering can have
before the existing clusters have to be merged. The default is set to
*10*.
- ``stream_speed/decay_rate``: These determine the fading factor of the
clusters. In this implementation, the fading function is constant derived
from the default values of *10* and *0.1*, respectively.
- ``remove_threshold``: This sets the lower bound for each cluster's weight
before they are considered to be removed. The default is set to *0.1*.
- ``merge_threshold``: This determines whether two close clusters can be merged
togther. The default is set to *1.25*.
- ``radius_threshold``: This determines the minimum range from an existing
cluster that a new data must be in order to be merged into one. The default
is set to *3.0*.
- ``active_threshold``: This sets the minimum weight of each cluster before
they are considered active. The default is set to *5.0*.
An example of setting these parameters:
.. code-block:: python
from estream import EStream
from sklearn.datasets.samples_generator import make_blobs
estream = EStream(max_clusters=5,
merge_threshold=0.5,
radius_threshold=1.5,
active_threshold=3.0)
data, _ = make_blobs()
estream.fit(data)
------------
Installation
------------
Currently, the package is only available through either ``PyPI``:
.. code-block:: bash
pip install estream
or a manual install:
.. code-block:: bash
wget https://github.com/mickeycj/estream/archive/master.zip
unzip master.zip
rm master.zip
cd estream-master
python setup.py install
--------------
Help & Support
--------------
Currently, there is no dedicated documentation available, so any questions or
issues can be asked via my `email <chanonjenakom@gmail.com>`_.
--------
Citation
--------
If you make use of this software for your work, please cite the paper from the
Advanced Data Mining and Applications: Third International Conference:
.. code-block:: bibtex
@inproceedings{inproceedings,
author = {Udommanetanakit, Komkrit, and Rakthanmanon, Thanawin and Waiyamai, Kitsana},
year = {2007},
month = {08},
pages = {605-615},
title = {E-Stream: Evolution-Based Technique for Stream Clustering},
volume = {4632},
doi = {10.1007/978-3-540-73871}
}
Moreover, this implementation is based on a MOA implementaion of E-Stream (and
other related algorithms) by `David Ratier <https://gitub.com/ratierd>`_. The
original source code can be found in this `repository <https://gitub.com/ratierd/MOA>`_.
-------
License
-------
The ``estream`` package is under the GNU General Public License.
------------
Contributing
------------
Contributions are always welcome! Everything ranging from code to notebooks and
examples/documentation will be very valuable to the growth of this project. To
contribute, please `fork this project <https://github.com/mickeycj/estream/issues#fork-destination-box>`_
, make your changes and submit a pull request. I will do my best to fix any
issues and merge your code into the main branch.
:Author: Chanon Jenakom
:Version: 0.0.3
:Dedicated: To DAKDL, Kasetsart University
نحوه نصب
نصب پکیج whl estream-0.0.3:
pip install estream-0.0.3.whl
نصب پکیج tar.gz estream-0.0.3:
pip install estream-0.0.3.tar.gz