annoviko/pyclustering

[pyclustering.cluster] STING algorithm

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#364 opened on Sep 13, 2017

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Description

Introduction STING (a STatistical INformation Grid approach) clustering algorithm.

The general idea is to divide spatial aria into rectangular cells at different levels of resolution which forms tree structure. Statistical information of each cell is calculated (mean, count, standard deviation, min, max) and type of distribution is also calculated (normal, uniform). After that regions queries are processed.

Resources

  1. Lecture: https://www.coursera.org/learn/cluster-analysis/lecture/vuf0F/5-5-sting-a-statistical-information-grid-approach
  2. Article: STING: A statistical information grid approach to spatial data mining.

Description General functionality that should be provided by the algorithm:

  1. Parallel implementation of query processing.
  2. Following interface should be used:
class sting:
   def process():
     pass;
   
   def get_clusters():
      pass;
  1. Visualization of the tree.
  2. Visualization of the queries.

What should be also done:

  • Unit-tests for the algorithm must be implemented: 'pyclustering/cluster/tests/unit/ut_sting.py'
  • Examples for the algorithm must be implemented: 'pyclustering/cluster/examples/sting_examples.py'
  • Doxygen documentation must be written for each public method, class, variable and it should be switched on in 'docs/doxygen_conf_pyclustering'

Contributor guide

Research direction
Study the STING clustering algorithm. Implement a grid hierarchy with multiple resolution levels, compute statistical information (mean, count, standard deviation, min, max, distribution type) for each cell, and support parallel query processing. Follow the required interface with process() and get clusters() methods. Implement unit tests in pyclustering/cluster/tests/unit/ut sting.py and examples in pyclustering/cluster/examples/sting examples.py. Add Doxygen documentation for all public methods and classes.
Tech stack
python
Domain
data
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50