Search
Search Results
-
Semantic Spectral Clustering with Contrastive Learning and Neighbor Mining
Deep spectral clustering techniques are considered one of the most efficient clustering algorithms in data mining field. The similarity between...
-
Clustering and Classification of Red Wines According to Physical-Chemical Properties Using Data Mining Methods
AbstractThe data on 178 samples of Italian red wines taken from the public machine learning repository UCI have been studied. Computer analysis of 13...
-
Clustering-based gradual pattern mining
Generally, the classical problem of gradual pattern mining involves generating pattern candidates and determining the number of concordant object...
-
Construction of Data Mining Model of CRM Marketing Based on Big Data Clustering Analysis
In the current competitive business landscape, the significance of Customer Relationship Management (CRM) has become increasingly prominent, as...
-
Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review
This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM),...
-
An Efficient Framework for Web Content Mining Systems Using Improved CD-PAM Clustering and the A-CNN Technique
The World Wide Web's expansion (WWW) has made finding appropriate information difficult, and web classification has emerged as an alternative...
-
Accelerated Sequential Data Clustering
Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data,...
-
Clustering customer orders in a smart factory using sequential pattern mining
In a smart factory, setting a production plan, relocating production equipment, and producing small batches of various products in real-time at a low...
-
An Information Entropy–based Risk (IER) Index of Mining Safety Using Clustering and Statistical Methods
In recent decades, the mining industry in the United States has made significant strides in reducing accidents and injuries. While these improvements...
-
A survey on quantum data mining algorithms: challenges, advances and future directions
Data mining has reached a state that is difficult to break through, while the scale of data is growing rapidly, due to the lack of traditional...
-
Split incremental clustering algorithm of mixed data stream
Clustering has been recognized as one of the most prominent functions in data mining. It aims to partition a given set of elements into homogeneous...
-
Data Mining as Generalization: A Formal Model
The model we present here formalizes the definition of Data Mining as the process of information generalization. In the model the Data Mining...
-
Adaptive Trajectory Data Stream Clustering
Trajectory data mining is a field that focuses on analyzing and extracting insights from the movement patterns of objects over time. The realm of...
-
Clustering from Data Streams
Clustering is one of the most popular data mining techniques. In this article, we review the relevant methods and algorithms for designing cluster...
-
A smart intelligent approach based on hybrid group search and pelican optimization algorithm for data stream clustering
Big data applications generate a huge range of evolving, real-time, and high-dimensional streaming data. In many applications, data stream clustering...
-
Scalable big earth observation data mining algorithms: a review
Enormous amount of earth information, gathered from satellite sensors, simulations, and other resources, are collectively referred to as Big Earth...
-
Excel and Data Mining
Let’s get right to the topic. Why do we need to learn Excel in our data mining endeavor? It is true that there are some outstanding data mining...
-
Frequent item-set mining and clustering based ranked biomedical text summarization
The difficulty of deriving value out of vast available scientific literature in a condensed form lead us to look for a proficient theme based...
-
A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms
The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks...
-
Data clustering: application and trends
Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no...
