Javascript must be enabled to continue!

A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering

EPRA JOURNALS

Aastha Gupta Himanshu Sharma Anas Akhtar

EPRA International Journal of Multidisciplinary Research (IJMR)

2021

Title: A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

Description:

Clustering is the process of arranging comparable data elements into groups.

One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results.

This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages.

This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments.

The outcomes are described in terms of big datasets.

The focus of this study is on clustering algorithms with the WEKA data mining tool.

Clustering is the process of dividing a big data set into small groups or clusters.

Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics.

It’s a data-modeling technique that provides a clear image of your data.

Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets.

KEYWORDS: data clustering, weka , k-means, hierarchical clustering.

Back

In a comprehensive and at times critical manner, this volume seeks to shed light on the development of events in Western (i.e., European and North American) comparative literature ...

The Kernel Rough K-Means Algorithm

Background: Clustering is one of the most important data mining methods. The k-means (c-means ) and its derivative methods are the hotspot in the field of clustering research in re...

Clustering Analysis of Data with High Dimensionality

Clustering analysis has been widely applied in diverse fields such as data mining, access structures, knowledge discovery, software engineering, organization of information systems...

Image clustering using exponential discriminant analysis

Local learning based image clustering models are usually employed to deal with images sampled from the non‐linear manifold. Recently, linear discriminant analysis (LDA) based vario...

Parallel density clustering algorithm based on MapReduce and optimized cuckoo algorithm

In the process of parallel density clustering, the boundary points of clusters with different densities are blurred and there is data noise, which affects the clustering performanc...

Streaming Hierarchical Clustering Based on Point-Set Kernel

Abstract Hierarchical clustering produces a cluster tree with different granularities. As a result, hierarchical clustering provides richer information and insight into a d...

Research on a microseismic signal picking algorithm based on GTOA clustering

Abstract. Clustering is one of the challenging problems in machine learning. Adopting clustering methods for the picking of microseismic signals has emerged as a new approach. Howe...

MR-DBIFOA: a parallel Density-based Clustering Algorithm by Using Improve Fruit Fly Optimization

<p>Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, the density-based clustering algorithm faces three challenging ...

Email:
Password:

Email:

A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

Related Results