Relevance (law)

Metric (unit)

Consensus clustering

Biology

Correlation clustering

Image (mathematics)

Similarity (geometry)

Euclidean distance

Data mining

Exploratory data analysis

Political science

Paleontology

Artificial intelligence

Economics

Fuzzy clustering

Set (abstract data type)

Clustering high-dimensional data

Programming language

Operations management

Context (archaeology)

Computer science

Ambiguity

Data set

Cluster analysis

Constrained clustering

CURE data clustering algorithm

Procedia Computer Science

Clustering is performed to get insights into the data whose volume makes it problematic for analysis by humans. Due to this, clustering algorithms have emerged as meta learning tools for performing exploratory data analysis. A Cluster is defined as a set of objects which have a higher degree of similarity to each other compared to objects not in the same set. However there is ambiguity regarding a suitable similarity metric for clustering. Multiple measures have been proposed related to quantifying similarity such as euclidean distance, density in data space etc. making clustering a multi-objective optimization problem. In this paper, different clustering approaches are studied from the theoretical perspective to understand their relevance in context of massive data-sets and empirically these have been tested on artificial benchmarks to highlight their strengths and weaknesses.

Empirical Analysis of Data Clustering Algorithms