OPTICS algorithm

Introduction

An implement of OPTICS(Ordering Points To Identify The Clustering Structure) algorithm in python

Quick Start

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from optics import OPTICS

OPTICS class need a pandas DataFrame to construct, the by-passed DataFrame's labels colum should have been trimmed off. The native distance calculating method was only designed for contineous attributes. If need to handle nominal or categorical attributes, re-write the distance calculating function.

df = pd.read_table("dataset.txt")
df1 = df.iloc[:,:-1]
model = OPTICS(df1)

Run the ordering method, this could take some minutes when deal with a large amount of data. 2 Arguments need to be passed in, the Eps for max epsilon and MinPts for min points number. Usually, Eps could be set with inf and MinPts with the dimensions of data entries + 1.

Notice: the MinPts does NOT contain the core point itself

model.optics(Eps=float('inf'), MinPts=4)

Now the ordering result should be written to model.result_queue, e.g. model.result_queue[0] is 17 means data entry df1.iloc[17,:] is the first element of ordering result queue.

And in model.core_distances and model.reachabel_distances are the cd and rd of every point accroding to the order of dataframe (not result queue).

Accroding to spirit of OPTICS, now an ordered reachable distance graph should be painted to see which Eps we should choose to get the best clustering result. This module dosen't provide visualization method, a 3rd-party plot lib is needed such as matplotlib.

Once you decide which Eps to use, you can extract the final clustering result into model.cluster_labels, it's ordered by the dataframe instead of model.result_queue.

model.cluster_extract(Eps=4)

Dependencies

1. python after version 3.5
2. numpy
3. pandas
4. matplotlib