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Special Issue on Advances in Intelligent Systems
2010
International Journal of Hybrid Intelligent Systems
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In the third paper, "Evolutionary Algorithms for Relational Clustering," the trek for an underlying structure in relational data is addressed. ...
The second paper, "Measures for Unsupervised Fuzzy-Rough Feature Selection," is focused on a recurrent important problem: feature selection. This is * Corresponding author. ...
doi:10.3233/his-2010-0116
fatcat:ischwcoo4ve2tjucjqyft4mjle
Hybrid learning machines
2009
Neurocomputing
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The algorithm is used for clustering gene expression data sets. In the fifth paper, Herrero et al. propose a novel hybrid artificial intelligent system for intrusion detection system. ...
Faceli et al. in the fourth paper illustrate an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. ...
doi:10.1016/j.neucom.2009.02.017
fatcat:jatayx5ec5h5hd72bjrlw2mhie
Hybridization of K-means with improved firefly algorithm for automatic clustering in high dimension
[article]
2023
arXiv
pre-print
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Also, previously, so many meta-heuristic swarm intelligence algorithms inspired by nature have been employed to handle the automatic data clustering problem. ...
Thus, our study proposed an enhanced firefly, i.e., a hybridized K-means with an ODFA model for automatic clustering. ...
E.g. in artificial intelligence as well as in data mining [8] [9] . Researchers have proposed several partitioning-based heuristic algorithms from last 2-3 decades to solve the clustering problems. ...
arXiv:2302.10765v1
fatcat:lio24hvo35dyrasp5gypahrd6i
Machine Learning Algorithms Applied to System Security: A Systematic Review
2020
Asian journal of applied science and technology
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analysis using text clustering in the large volume of data ...
Unsupervised learning Unsupervised learning algorithm is a machine learning algorithm that required unlabeled datasets for training and Machine learning algorithms applied to system security In [7] presented ...
doi:10.38177/ajast.2020.4311
fatcat:jrtmzlswvjbtpms4m3c5hgan4q
Hybrid quantum-classical unsupervised data clustering based on the Self-Organizing Feature Map
[article]
2023
arXiv
pre-print
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2020 pre-print arXiv:2009.09246v1 fatcat:e2h7aqqkhvd7dpwpsaqbx37ida
Here, we introduce an algorithm for quantum assisted unsupervised data clustering using the self-organizing feature map, a type of artificial neural network. ...
We compare the results with the classical algorithm on a toy example of unsupervised text clustering. ...
In this paper, we develop a hybrid quantum-assisted SOFM (QASOFM) and apply it to the data clustering problem in an unsupervised manner. ...
arXiv:2009.09246v2
fatcat:n67y6nxg5fh23gptp23x2zewqy
Computational Intelligence Algorithms and DNA Microarrays
[chapter]
2008
Studies in Computational Intelligence
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In this chapter, we present Computational Intelligence algorithms, such as Neural Network algorithms, Evolutionary Algorithms, and clustering algorithms and their application to DNA microarray experimental ...
data analysis. ...
Then, we perform an extensive evaluation of various clustering algorithms for supervised as well as unsupervised classification of the data sets. ...
doi:10.1007/978-3-540-76803-6_1
fatcat:pyurpuotivff7hz6hnngnsbwwq
Hybrid Artificial Chemical Reaction Optimization Algorithm for Cluster Analysis
2020
Procedia Computer Science
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In present work, a hybrid version of the artificial chemical reaction optimization algorithm (HACRO) is proposed to optimize clustering problems. ...
In present work, a hybrid version of the artificial chemical reaction optimization algorithm (HACRO) is proposed to optimize clustering problems. ...
Prakash and Singh have reported a hybrid Gbest-guided artificial bee colony algorithm for clustering problems [12] . ...
doi:10.1016/j.procs.2020.03.312
fatcat:fjq3rwodl5chnoun5ffpiuz6ru
Artificial Intelligence and Big Data in Fraud Detection
2021
EURAS Journal of Engineering and Applied Sciences
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artificial intelligence are discussed with effectiveness usage for the future applications. ...
Supervised machine learning, unsupervised machine learning or semi-supervised machine learning as well as adaptive machine learning techniques against adaptive attacks with the advantage of big data and ...
of big data and artificial intelligence can be used effectively for the future applications. ...
doi:10.17932/ejeas.2021.024/ejeas_v01i2001
fatcat:l7zdy6bfujfllkdb4pcrmpzbou
Introduction to Machine Learning and Its Applications: A Survey
2020
Zenodo
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It is a subset of Artificial Intelligence (AI), and consists of the more advanced techniques and models that enable computers to figure things out from the data and deliver. ...
It is a field of learning and broadly divided into supervised learning, unsupervised learning, and reinforcement learning. There are many fields where the Machine learning algorithms are used. ...
Output of the unsupervised learning is group or cluster of data having similar characteristics. Types of unsupervised learning are clustering and association. ...
doi:10.5281/zenodo.3775091
fatcat:zbzxbuycxzerdl6drc276t6ifu
A Survey on Anomaly Detection and Diagnosis Problem in the Space System Operation
2019
Zeki sistemler teori ve uygulamaları dergisi
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Supervised/unsupervised (machine learning) anomaly detection approaches and data mining technology are the most used methods. ...
Various intelligent anomaly detection methods are proposed in the literature. ...
Biswas, Gautam (Biswas et al., 2016) have used unsupervised learning algorithm to cluster time series data. ...
doi:10.38016/jista.509532
fatcat:v2hasxffsncctceccl6hmwb6i4
Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature
2014
Global Journal of Health Science
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We reviewed studies that performed data mining techniques for detecting health care fraud and abuse, using supervised and unsupervised data mining approaches. ...
The scale of this problem is large enough to make it a priority issue for health systems. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. ...
Acknowledgement We thank the Tehran University of Medical Sciences for funding the study coded 17311.
Competing Interests Not declared. ...
doi:10.5539/gjhs.v7n1p194
pmid:25560347
pmcid:PMC4796421
fatcat:ihkxwrhmbfewti2jtu7tkxrdva
PSO aided k-means clustering
2011
Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11
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Clustering is a fundamental and hence widely studied problem in data analysis. ...
The standard k-Means algorithm is hybridized with Particle Swarm Optimization. ...
Problem
k-Means PSO
Table 2 : 2 Results for unsupervised clustering. ...
doi:10.1145/2001576.2001742
dblp:conf/gecco/BreabanL11
fatcat:e2bv7xajj5ellg5jnixwxf36u4
Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends
2021
Electronics
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These methods include supervised, unsupervised and reinforcement techniques. ...
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. ...
The data for the unsupervised ML models used in 6G problems are listed in Table 7 . ...
doi:10.3390/electronics10222786
fatcat:6umid7qnabdttkjyhglpxjpwpm
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
2019
IEEE Access
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In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine ...
Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, ...
Moreover, sufficient quantity of data is required for creating distinguishable clusters; withstanding enough data for classification problem, there exist a problem of gray area between clusters and creation ...
doi:10.1109/access.2019.2916648
fatcat:xutxh3neynh4bgcsmugxsclkna
Special volume on Data Mining
2009
Annals of Operations Research
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Five papers address unsupervised learning problems, two for pre-processing complex data structures, two applied to business applications, and one applicable to multivariate statistical process control. ...
To provide a forum bringing these groups together, the first INFORMS Workshop on Artificial Intelligence and Data Mining, a collaboration between the INFORMS Data Mining and Artificial Intelligence subdivisions ...
the problem of detecting changes in multivariate time series data into a supervised learning problem. Two papers address issues in support vector methods. ...
doi:10.1007/s10479-009-0625-1
fatcat:cihjk5hmn5h5dphwcv52o4c4fe
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