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COMPARATIVE STUDY BETWEEN DENSITY BASED CLUSTERING
- DBSCAN AND OPTICS
1
PRANJAL DUBEY, 2ANAND RAJAVAT
1
PG Scholar, Department of CSE, SVITS, Indore, India
2
HOD, Department of CSE, SVITS, Indore, India
E-mail: [email protected], [email protected]
Abstract— Data mining is process of retrieving data and patterns from large database. Clustering is a phase of data mining
that cumulates the data and finds a proven structure from the database. A good clustering approach plays a major role in
detecting clusters of arbitrary shapes. In this paper I have discuss about the Density Based Clustering Spatial Clustering of
Applications with Noise (DBSACN) which finds out clusters of different shapes and size from a large database and
improves scalability and efficiency in a multiphase clustering. With this Ordering Points to Identify the Clustering Structure
(OPTICS) have been compared to identify similar objects based on their density, here one produces clusters and the other
outputs augmented ordering representing density-based structure of a database. The parameters and their optimisations are
also discussed.
Keywords— Clustering, density-based clustering, DBSCAN, OPTICS.
An innovative technique which is used to compare in
between two different clustering algorithms
(DBSCAN
and
SNN)
described
several
implementations of the DBSCAN and SNN
algorithms, two density-based clustering algorithms.
These implementations can be used to cluster sets of
points based on their spatial density. The results
obtained through the use of these algorithms show
that SNN performs better than DBSCAN since it can
detect clusters with different densities while
DBSCAN cannot [3].
Many clustering algorithm have been proposed,
seldom was focused on high dimensional and
incremental databases. An incremental approach on
Grid Density-Based Clustering Algorithm (GDCA)
discovers clusters with arbitrary shape in spatial
databases. It first partitions the data space into a
number of units, and then deals with units instead of
points. Only those units with the density no less than
a given minimum density threshold are useful in
extending clusters [4].
An innovative approach presents a new density-based
clustering algorithm, ST-DBSCAN, which is based
on DBSCAN. It proposes three marginal extensions
to DBSCAN related with the identification of (i) core
objects, (ii) noise objects, and (iii) adjacent clusters.
In contrast to the existing density-based clustering
algorithms, this algorithm has the ability of
discovering clusters according to non-spatial, spatial
and temporal values of the objects [5].
The new concept is presented on clustering technique
which provides an effective method for Clustering
Incremental Gene Expression data. It is designed
based on density based approach where the efficiency
of GenClus in detecting quality clusters over gene
expression data. This work presents a density based
clustering approach which finds useful subgroups of
highly coherent genes within a cluster and obtains a
hierarchical structure of the dataset where the sub
clusters give the finer clustering of the dataset [6].
I. INTRODUCTION
Clustering is the primary data mining technique. It
can even be a stand-alone tool or a pre-processing
step in other data mining applications. Clustering is a
process of evolving similar objects from database and
grouping them into valid clusters. The different
cluster formation follows different attributes and
algorithm to form clusters therefore it may results in
different outcomes. Clustering algorithm is applied in
many fields: pattern recognition, information
retrieval, image processing, machine learning.
Density-based algorithm is simple and high efficiency
algorithm [1].
Various methods are best suited for different
databases. Here we are dealing with DBSCAN and
OPTICS which are used to detect clusters of different
densities, shapes and sizes in spatial datasets with
noise.
The paper is structured in following sections: section
2 presents review density-based clustering. Section 3
discusses about DBSCAN and clustering over it.
Section 4 discusses about OPTICS and cluster
structuring and formation. Section 5 concludes with
parameters with respect to optimizing performance of
density-based algorithms.
II. LITERATURE REVIEW
Density-based clustering is highlighted by number of
applications. Significant work has been done in this
field of Density based clustering. One approach has
been developed the incremental clustering for mining
large database. This approach present the first
incremental clustering algorithm based on DBSCAN
which is applicable on any database containing data
in a metric space. Due to the density-based nature of
DBSCAN, the insertion or deletion of an object
affects the current clustering only in the
neighbourhood of this object. Thus, efficient
algorithm scan be given for incremental insertions
and deletions to an existing clustering [2].
Proceedings of 64th IRF International Conference, 16th October, 2016, Pune, India, ISBN: 978-93-86291-14-1
58
Comparative Study Between Density Based Clustering - DBSCAN and Optics
Proceedings of 64th IRF International Conference, 16th October, 2016, Pune, India, ISBN: 978-93-86291-14-1
59
Comparative Study Between Density Based Clustering - DBSCAN and Optics
Proceedings of 64th IRF International Conference, 16th October, 2016, Pune, India, ISBN: 978-93-86291-14-1
60
Comparative Study Between Density Based Clustering - DBSCAN and Optics
ACKNOWLEDGMENT
The authors would like to appreciate their teacher’s
for their valuable guidance and would thanks to their
family and friends for their valuable support.
REFERENCES
[1]
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Proceedings of 64th IRF International Conference, 16th October, 2016, Pune, India, ISBN: 978-93-86291-14-1
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