A Study on Hierarchical Clustering Algorithms
Iasir Journals
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
AI
AI
This study explores various hierarchical clustering algorithms and their applications in video data mining. The paper dives into the challenges posed by the unique characteristics of video content, which limit existing video understanding techniques. A comparative analysis of algorithms such as BIRCH, CURE, and CHAMELEON is conducted, highlighting their features and suggesting areas for enhancement. Conclusions are drawn on their suitability for processing video data, contributing to future algorithm development in this domain.
Related papers
Unsupervised learning : Clustering
Unsupervised learning, 2022
Unsupervised learning is a type of machine learning where the model is not provided with labeled data. Clustering is a specific technique within unsupervised learning that groups similar data points together, without the use of labeled data. Clustering is commonly used in applications such as market segmentation, image segmentation, and anomaly detection. Some popular clustering algorithms include kmeans, hierarchical clustering, and density-based clustering.
Clustering Analysis using an Unsupervised Machine Learning Method
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
Artificial Intelligence (AI) and Machine Learning (ML), which are becoming a part of interest rapidly for various researchers. ML is the field of Computer Science study, which gives capability to learn without being absolutely programmed. This work focuses on the standard k-means clustering algorithm and analysis the shortcomings of the standard k-means algorithm. The k-means clustering algorithm calculates the distance between each data object and not all cluster centres in every iteration, which makes the efficiency of clustering is high. In this work, we have to try to improve the k-means algorithm to solve simple data to store some information in every iteration, which is to be used in the next interaction. This method avoids computing distance of data object to the cluster centre repeatedly, saving the running time. An experimental result shows the enhanced speed of clustering, accuracy, reducing the computational complexity of the k-means. In this, we have work on iris dataset extracted from Kaggle.
8 Cluster Analysis: Basic Concepts and Algorithms
Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both. If meaningful groups are the goal, then the clusters should capture the natural structure of the data. In some cases, however, cluster analysis is only a useful starting point for other purposes, such as data summarization. Whether for understanding or utility, cluster analysis has long played an important role in a wide variety of fields: psychology and other social sciences, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. There have been many applications of cluster analysis to practical problems. We provide some specific examples, organized by whether the purpose of the clustering is understanding or utility. Clustering for Understanding Classes, or conceptually meaningful groups of objects that share common characteristics, play an important role in how people analyze and describe the world. Indeed, human beings are skilled at dividing objects into groups (clustering) and assigning particular objects to these groups (classification). For example, even relatively young children can quickly label the objects in a photograph as buildings, vehicles, people, animals , plants, etc. In the context of understanding data, clusters are potential classes and cluster analysis is the study of techniques for automatically finding classes. The following are some examples:
Hierarchical Clustering
Undergraduate Topics in Computer Science, 2011
Clustering techniques have a wide use and importance nowadays. This importance tends to increase as the amount of data grows and the processing power of the computers increases. Clustering applications are used extensively in various fields such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing.There are several algorithms and methods have been developed for clustering problem. But problem are always arises for finding a new algorithm and process for extracting knowledge for improving accuracy and efficiency. This type of dilemma motivated us to develop new algorithm and process for clustering problems. There are several another issue are also exits like cluster analysis can contribute in compression of the information included in data. In several cases, the amount of available data is very large and its processing becomes very demanding. Clustering can be used to partition data set into a number of "interesting" clusters. Then, instead of processing the data set as an entity, we adopt the representatives of the defined clusters in our process. Thus, data compression is achieved. Cluster analysis is applied to the data set and the resulting clusters are characterized by the features of the patterns that belong to these clusters. Then, unknown patterns can be classified into specified clusters based on their similarity to the clusters' features. Useful knowledge related to our data can be extracted [1].
A Review Paper on Clustering in Data Mining
Clustering is a process of keeping similar data into groups.Objects within the cluster/group have high similarity in comparison to one another but are very dissimilar to objects of other clusters. Clustering is an unsupervised learning technique as every other problem of this kind; it deals with finding a structure in a collection of unlabeled data. Types of clustering methods are–hierarchical and partitioning based. In this paper clustering and its methods are discussed.
Clustering2
Data mining is an area of computer and information science with large perspective of knowledge discovery from large database or dataset. Various types of disciplines are available under data mining and clustering or the unsupervised learning in particular. Clustering is a division of data into similar groups; each similar group is called a cluster. Object in a cluster are similar or close to each other. Clustering algorithms can be implemented via number of different approaches. We conducted the comparison on WEKA (The Waikato Environment for Knowledge Analysis) that is open source. This paper shows that study and comparison between different clustering algorithmspartitioning method, hierarchical method and density based method. Here we have used parameter cluster instance, iterations, sum of squared errors, time taken, etc. for prediction of forest fire.
Clustering Model of Multimedia Data
With the recent advances in electronic imaging, video devices, storage, networking and computer power, the amount of multimedia has grown enormously, and multimedia data management has become a popular way of discovering new knowledge from such a large data sets. This paper utilizes the Rough set theory to cluster multimedia data into three classes. The clustering results are then used to manage multimedia data. The experimental results show that the proposed model is effective to classify the media types of multimedia data and obtain 0.98% of average retrieval performance. The paper used Rosetta software which is based on rough set theory to process the data.
International Journal of Computer Science and Mobile Computing COMPARATIVE STUDY OF VARIOUS CLUSTERING TECHNIQUES
Clustering is a process of dividing the data into groups of similar objects and dissimilar ones from other objects. Representation of data by fewer clusters necessarily loses fine details, but achieves simplification. Data is model by its clusters. Clustering plays an significant part in applications of data mining such as scientific data exploration, information retrieval, text mining, city-planning, earthquake studies, marketing, spatial database applications, Web analysis, marketing, medical diagnostics, computational biology, etc. Clustering plays a role of active research in several fields such as statistics, pattern recognition and machine learning. Data mining adds complications to very large datasets with many attributes of different types to clustering. Unique computational requirements are imposed on relevant clustering algorithms. A variety of clustering algorithms have recently emerged that meet the various requirements and were successfully applied to many real-life data mining problems. 1. INTRODUCTION The goal of this study is to provide a universal review of various clustering techniques in data mining. A technique for grouping set of data objects into multiple groups/clusters so that objects within the cluster have high similarity, but are very dissimilar to objects in the other clusters is known as 'clustering'. Clustering is a technique of removing any attribute that is known to be very noisy or not interesting. Dissimilarities and similarities are estimated based on the attribute values representing the objects. Clustering algorithms are used to organize and categorize data for data concretion and model construction, detection of deviation, etc. Common approach of clustering is to find centroid that will represent a certain cluster. Cluster centre will be represented with input vector which measures a similarity unit between input vector and all cluster centroid and determining which cluster is nearest or most similar one. To gain penetration into the data distribution or as a preprocessing step for other data mining algorithms operating on the detected clusters, cluster analysis can be used as a standalone data mining tool. Clustering is unsupervised learning of a hidden data concept. Data mining deals with large databases that can enforce on clustering analysis for additional severe computational requirements. These challenges led to the emergence of powerful broadly applicable data mining clustering methods. Many clustering algorithms have been developed and are categorized from several aspects such as partitioning methods, hierarchical methods and grid-based methods. Data set can be either numeric or
Data Clustering
Emerging Techniques and Technologies
The chapter provides a survey of some clustering methods relevant to the clustering document collections and, in consequence, Web data. We start with classical methods of cluster analysis which seem to be relevant in approaching to cluster Web data. The graph clustering is also described since its methods contribute significantly to clustering Web data. A use of artificial neural networks for clustering has the same motivation. Based on previously presented material, the core of the chapter provides an overview of approaches to clustering in the Web environment. Particularly, we focus on clustering web search results, in which clustering search engines arrange the search results into groups around a common theme. We conclude with some general considerations concerning the justification of so many clustering algorithms and their application in the Web environment.
Survey of Clustering Data Mining Techniques
Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others.
Related papers
Issues,Challenges and Tools of Clustering Algorithms
2011
Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively
Data clustering: a review
ACM Computing Surveys, 1999
Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
A detailed study of clustering algorithms
2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2017
the foremost illustrative task in data mining process is clustering. It plays an exceedingly important role in the entire KDD process also as categorizing data is one of the most rudimentary steps in knowledge discovery. It is an unsupervised learning task used for exploratory data analysis to find some unrevealed patterns which are present in data but cannot be categorized clearly. Sets of data can be designated or grouped together based on some common characteristics and termed clusters , the mechanism involved in cluster analysis are essentially dependent upon the primary task of keeping objects with in a cluster more closer than objects belonging to other groups or clusters. Depending on the data and expected cluster characteristics there are different types of clustering paradigms. In the very recent times many new algorithms have emerged which aim towards bridging the different approaches towards clustering and merging different clustering algorithms given the requirement of handling sequential ,extensive data with multiple relationships in many applications across a broad spectrum. Various clustering algorithms have been developed under different paradigms for grouping scattered data points and forming efficient cluster shapes with minimal outliers. This paper attempts to address the problem of creating evenly shaped clusters in detail and aims to study, review and analyze few clustering algorithms falling under different categories of clustering paradigms and presents a detailed comparison of their efficiency, advantages and disadvantages on some common grounds. This study also contributes in correlating some very important characteristics of an efficient clustering algorithm.
A Review on Clustering Method based on Unsupervised Learning Approach
International Journal of Computer Applications
Data mining main goal of information find in large dataset or the data mining process is to take out information from an outsized data set and transform it into a clear kind for any use. group is vital in information analysis and data processing applications. it's the task of clustering a group of objects in order that objects within the same group are additional kind of like different or one another than to those in other teams (clusters).speedy recovery of the related data from databases has invariably been a big issue. There are several techniques are developed for this purpose; in among information cluster is one amongst the key techniques. The method of making very important data from a large quantity of information is learning. It may be classified into 2 like supervised learning and unsupervised learning. Group could be a quite unsupervised data processing technique. It describes the overall operating behavior, the methodologies followed by these approaches and therefore the parameters that have an effect on the performance of those algorithms. a review of cluster and its completely different techniques in data processing is completed.
A Survey of Clustering Algorithms
Clustering is a technique of grouping similar data objects in one group and dissimilar data objects in other group. Clustering or data grouping is the key technique of the data mining. It is an unsupervised learning task where one seeks to identify a finite set of categories termed clusters to describe the data. Grouping of data into clusters aims to maximize the intra class similarity and also minimize the inter class similarity. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. This paper aims to provide a brief overview and complexities of various clustering algorithms.
CLUSTERING TO DESCRIBE THE DATA
Clustering is the process of finding meaningful groups in data. In clustering , the objective is not to predict a target class variable, but to simply capture the possible natural groupings in the data. For example, customers of a company can be grouped based on the purchase behavior. In recent years, clustering has even found its use in political elections (Pearson & Cooper, 2012). Prospective electoral voters can be clustered into different groups so that candidates can tailor messages to resonate within each group. Before we proceed, we should further clarify the difference between classification and clustering using a simple example. Categorizing a given voter as a soccer mom (a known user group) or not is a supervised learning task of classification task. Segregating a population of electorates into different groups, based on similar demographics is an unsupervised learning task of clustering. The process of identifying whether a data point belongs to a particular known group is classification. The process of dividing data into meaningful groups is clustering. In many cases one would not know ahead of what groups to look for and thus the identified groups might be difficult to explain. These identified groups are referred to as clusters. The data mining task of clustering can be used in two different classes of applications: to describe a given data set and as a preprocessing step for other predictive algorithms.
AN OVERVIEW ON CLUSTERING METHODS
IOSR Journal of Engineering, 2012
Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the process of grouping similar objects into different groups, or more precisely, the partitioning of a data set into subsets, so that the data in each subset according to some defined distance measure. This paper covers about clustering algorithms, benefits and its applications. Paper concludes by discussing some limitations.
Cluster Analysis – An Overview
TJPRC, 2013
Clustering analysis, also called segmentation analysis or taxonomy analysis, aims to identify homogeneous objects into a set of groups, named clusters, by given criteria. Clustering is a very important technique of knowledge discovery for human beings. It has a long history and can be traced back to the times of Aristotle .These days; cluster analysis is mainly conducted on computers to deal with very large-scale and complex datasets. With the development of computer-based techniques, clustering has been widely used in data mining, ranging from web mining, image processing, machine learning, artificial intelligence, pattern recognition, social network analysis, bio-informatics, geography, geology, biology, psychology, sociology, customers behaviour analysis, marketing to e-business and other fields.
Clustering Methods
Data Mining and Knowledge Discovery Handbook, 2005
This chapter presents a tutorial overview of the main clustering methods used in Data Mining. The goal is to provide a self-contained review of the concepts and the mathematics underlying clustering techniques. The chapter begins by providing measures and criteria that are used for determining whether two objects are similar or dissimilar. Then the clustering methods are presented, divided into: hierarchical, partitioning, density-based, model-based, grid-based, and soft-computing methods. Following the methods, the challenges of performing clustering in large data sets are discussed. Finally, the chapter presents how to determine the number of clusters.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.