# OPTICS: ordering points to identify the clustering structure

@inproceedings{Ankerst1999OPTICSOP, title={OPTICS: ordering points to identify the clustering structure}, author={Mihael Ankerst and Markus M. Breunig and Hans-Peter Kriegel and J{\"o}rg Sander}, booktitle={ACM SIGMOD Conference}, year={1999} }

Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does…

## 3,969 Citations

### A Cluster Algorithm Identifying the Clustering Structure

- Computer Science2008 International Conference on Computer Science and Software Engineering
- 2008

Both theory analysis and experimental results confirm CluICS can cluster data of varying density with automatic setting different parameters in different partitions and its efficiency is much higher than DBSCAN algorithm.

### An efficient density-based clustering for multi-dimensional database

- Computer Science2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS)
- 2017

A density-based clustering algorithm that adopts a divide-and-conquer strategy and presents a way to automatically determine the grid cell width, showing that the proposed algorithm efficiently finds accurate clusters in both low-dimensional and multi-dimensional databases.

### Adaptive Methods for Determining DBSCAN Parameters

- Computer Science
- 2014

The objective is to enhance the existing DBSCAN algorithm by automatically selecting the input parameters and to find the density varied clusters, and the proposed algorithm discovers arbitrary shaped clusters, requires noinput parameters and uses the same definitions of DBS CAN algorithm.

### ICA: An Incremental Clustering Algorithm Based on OPTICS

- Computer ScienceWireless Personal Communications
- 2015

A detailed comparison of ICA and OPTICS is presented and the results illustrate that ICA is much more suitable for clustering the dynamic datasets, i.e., some new data objects are added into the datasets as time goes on.

### ICA: An Incremental Clustering Algorithm Based on OPTICS

- Computer ScienceWirel. Pers. Commun.
- 2015

A detailed comparison of ICA and OPTICS is presented and the results illustrate that ICA is much more suitable for clustering the dynamic datasets, i.e., some new data objects are added into the datasets as time goes on.

### Improving OPTICS Algorithm with Imperialist Competitive Algorithm: Choosing Automatically Best Parameters

- Computer Science
- 2016

The main goal of this research is to use meta-heuristic methods especially Imperialist Competitive Algorithm (ICA) to precise estimation of these parameters (Ɛ, µ) so that they can apply to OPTICS Algorithm to achieve accurate and high quality clusters for any data sets.

### AutoEpsDBSCAN : DBSCAN with Eps Automatic for Large Dataset

- Computer Science
- 2013

The proposed enhanced algorithm can detect the clusters of varied density with different shapes and sizes from large amount of data which contains noise and outliers, requires only one input parameters and gives better output then the existing DBSCAN algorithm.

### An Empirical Evaluation of Density-Based Clustering Techniques

- Computer Science
- 2012

This paper shows the comparison of two density based clustering methods i.e. DBSCAN (15) & OPTICS (14) based on essential parameters such as distance type, noise ratio as well as run time of simulations performed aswell as number of clusters formed needed for a good clustering algorithm.

### Enhancing density-based clustering: Parameter reduction and outlier detection

- Computer ScienceInf. Syst.
- 2013

### HIERAR CHICAL CLUSTERING USING LEVEL SETS

- Computer Science
- 2012

This paper introduces a new concept which is used to develop a new recursive version of DBSCAN that can successfully perform hierarchical clustering, called Level-Set Clustering (LSC), and is able to produce the clustering result with the same O(n log n) time complexity.

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