A Review on Clustering and Outlier Analysis Techniques in Datamining

Fabiane,

doi:10.3844/ajassp.2012.254.258

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…To improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information, this study proposes effective pattern methods. For detailed survey of text mining, for clustering (Koteeswaran et al, 2012), a survey of evolutionary algorithm by Barros et al (2012) and survey of twenty of years of mixure of experts by Yuksel 2012 are recommended.…”

Section: Mathematical Model Of Clustering and Literature Surveymentioning

confidence: 99%

Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering

T.¹

2012

Journal of Computer Science

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As the engineering world are growing fast, the usage of data for the day to day activity of the engineering industry also growing rapidly. In order to handle and to find the hidden knowledge from huge data storage, data mining is very helpful right now. Text mining, network mining, multimedia mining, trend analysis are few applications of data mining. In text mining, there are variety of methods are proposed by many researchers, even though high precision, better recall are still is a critical issues. In this study, text mining is focused and conceptual mining model is applied for improved clustering in the text mining. The proposed work is termed as Meta data Conceptual Mining Model (MCMM), is validated with few world leading technical digital library data sets such as IEEE, ACM and Scopus. The performance derived as precision, recall are described in terms of Entropy, F-Measure which are calculated and compared with existing term based model and concept based mining model

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Section: Mathematical Model Of Clustering and Literature Surveymentioning

confidence: 99%

Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering

T.¹

2012

Journal of Computer Science

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“…The data mining is an emerging technique which applies many approaches and methods from another field of study and also the data mining is implemented in another area to learn hidden knowledge (Koteeswaran et al, 2012a). In this proposed work, Artificial Neuran Network (ANN) based unsupervised learning is used for learning text in the Virtual Mining Model.…”

Section: Methodsmentioning

confidence: 99%

Virtual Mining Model for Classifying Text Using Unsupervised Learning

Koteeswaran¹

2014

American Journal of Applied Sciences

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In real world data mining is emerging in various era, one of its most outstanding performance is held in various research such as Big data, multimedia mining, text mining etc. Each of the researcher proves their contribution with tremendous improvements in their proposal by means of mathematical representation. Empowering each problem with solutions are classified into mathematical and implementation models. The mathematical model relates to the straight forward rules and formulas that are related to the problem definition of particular field of domain. Whereas the implementation model derives some sort of knowledge from the real time decision making behaviour such as artificial intelligence and swarm intelligence and has a complex set of rules compared with the mathematical model. The implementation model mines and derives knowledge model from the collection of dataset and attributes. This knowledge is applied to the concerned problem definition. The objective of our work is to efficiently mine knowledge from the unstructured text documents. In order to mine textual documents, text mining is applied. The text mining is the sub-domain in data mining. In text mining, the proposed Virtual Mining Model (VMM) is defined for effective text clustering. This VMM involves the learning of conceptual terms; these terms are grouped in Significant Term List (STL). VMM model is appropriate combination of layer 1 arch with Analysis of Bilateral Intelligence (ABI). The frequent update of conceptual terms in the STL is more important for effective clustering. The result is shown, Artifial neural network based unsupervised learning algorithm is used for learning texual pattern in the Virtual Mining Model. For learning of such terminologies, this paper proposed Artificial Neural Network based learning algorithm.

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“…On the other hand, the techniques of unsupervised learning can be applied to generate decisions without having any trained models (Mishra & Soni, 2014) . The process of learning includes various techniques which are used for analysing, computation and implementation of data (Koteeswaran, Visu, & Janet, 2012) (Li, Roy, Khan, Wang, & Bai, 2012). Usually, learning is considered as the procedure for training the data set.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Artificial Intelligence in Bone Fracture Detection

Mishra¹,

Mishra

2022

International Journal of Reliable and Quality E-Healthcare

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The objective of the paper is to present the techniques of Artificial Intelligence based on deep learning that can be applied to detect fractures in bones on X-rays. The paper comprises of discussions of various entities. Initially, there is a discussion on data formulation and processing. Following which, distinguished image processing techniques are presented for fracture detection. Later, there is an analysis of conventional and current neural network methodologies for fracture detection techniques. Furthermore, there is a comparative analysis for the same. Finally, in the end, a discussion is presented in the paper regarding problems and challenges confronted by researchers for fracture detection. The study shows, deep learning techniques provide accuracy in the diagnosis than the conventional methods in fracture detection on X-rays. The paper leads to a path for the researchers to deal with difficulties and issues encountered with the fracture detection on X-rays while using deep learning techniques.

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A Review on Clustering and Outlier Analysis Techniques in Datamining

Cited by 13 publications

References 7 publications

Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering

Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering

Virtual Mining Model for Classifying Text Using Unsupervised Learning

Evolution of Artificial Intelligence in Bone Fracture Detection

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