Add to ORKGAbstract Cluster analysis has become one of the main tools used in extracting knowledge from data, which is known as data mining. In this area of data analysis, data of large dimensions are often processed, both in the number of objects and in the number of variables, which characterize the objects. Many methods for data clustering have been developed. One of the most widely used is a k-means method, which is suitable for clustering data sets containing large number of objects. It is based on finding the best clustering in relation to the initial distribution of objects into clusters and subsequent step-by-step redistribution of objects belonging to the clusters by the optimization function. The aim of this Ph.D. thesis was a comparison of ...
The goal of the article is to present the idea of discovering unusual data in large datasets in whic...
Data clustering techniques are valuable tools for researchers working with large databases of multiv...
Clustering has been one of the most widely studied topics in data mining and it is often the first s...
Advances in recent techniques for scientific data collection in the era of big data allow for the sy...
huge data is a big challenge. Clustering technique is able to find hidden patterns and to extract us...
As we know that clustering is a process for discovering groups and identifying interesting patterns....
The thesis is divided into five chapters. In the first two chapters I give the overview of clusterin...
The aim of this work is to compare different strategies to cluster large data sets. In particular, t...
Clustering is one of the most important research areas in the field of data mining. In simple words,...
Cluster analysis is the generic name of all those techniques which allow to aggregate n-units into k...
K-means clustering technique works as a greedy algorithm for partition the n-samples into k-clusters...
The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks...
Working with huge amount of data and learning from it by extracting useful information is one of the...
The paper focuses on the development of selected approaches in cluster analysis. There are recently ...
The traditional clustering algorithm, K-means, is famous for its simplicity and low time complexity....
The goal of the article is to present the idea of discovering unusual data in large datasets in whic...
Data clustering techniques are valuable tools for researchers working with large databases of multiv...
Clustering has been one of the most widely studied topics in data mining and it is often the first s...
Advances in recent techniques for scientific data collection in the era of big data allow for the sy...
huge data is a big challenge. Clustering technique is able to find hidden patterns and to extract us...
As we know that clustering is a process for discovering groups and identifying interesting patterns....
The thesis is divided into five chapters. In the first two chapters I give the overview of clusterin...
The aim of this work is to compare different strategies to cluster large data sets. In particular, t...
Clustering is one of the most important research areas in the field of data mining. In simple words,...
Cluster analysis is the generic name of all those techniques which allow to aggregate n-units into k...
K-means clustering technique works as a greedy algorithm for partition the n-samples into k-clusters...
The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks...
Working with huge amount of data and learning from it by extracting useful information is one of the...
The paper focuses on the development of selected approaches in cluster analysis. There are recently ...
The traditional clustering algorithm, K-means, is famous for its simplicity and low time complexity....
The goal of the article is to present the idea of discovering unusual data in large datasets in whic...
Data clustering techniques are valuable tools for researchers working with large databases of multiv...
Clustering has been one of the most widely studied topics in data mining and it is often the first s...