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Application of Data Mining Using Artificial Neural Network: Survey
2015
International Journal of Database Theory and Application
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Data mining models depend on task they accomplish: Association Rules, Clustering, Prediction, and Classification. Neural network is used to find pattern in data. ...
The categorization of Data Mining models depend on task they accomplish: Association Rules, Clustering, Prediction, and Classification. ...
From trained neural network for the extraction of the rules he makes use of the clustering genetic algorithm. ...
doi:10.14257/ijdta.2015.8.1.25
fatcat:sbcxvdwebnf3ro7e3zxhun3esi
Using neural networks for data mining
1997
Future generations computer systems
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The rst type of approach, often called rule extraction, i n volves extracting symbolic models from trained neural networks. ...
Neural-network methods are not commonly used for data-mining tasks, however, because they often produce incomprehensible models and require long training times. ...
Mark Craven is currently supported by D ARPA g r a n t F33615-93-1-1330. ...
doi:10.1016/s0167-739x(97)00022-8
fatcat:hhcf2iai7nbm7hz6yyxpxjcsv4
An Overview on the Use of Neural Networks for Data Mining Tasks
2011
Learning and Nonlinear Models
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This overview provides comments in the main aspects related to some of the most common neural network models in use today for data mining tasks. ...
The search for the more efficient models, the extraction of the hidden knowledge and the data mining application characteristics are examined. ...
The algorithms designed to perform such tasks are generally called algorithms for rule extraction from neural networks. ...
doi:10.21528/lnlm-vol9-no3-art5
fatcat:ln7zvjhsirhxbbh3f2l4jfurve
Design and Implementation of Rainfall Prediction Model using Supervised Machine Learning Data Mining Technique s
2021
Indian Journal of Data Mining
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Also, artificial neural network data mining techniques is used to design a rainfall prediction model. ...
In this paper, collection of weather data and pre-processing it for rainfall prediction model using Rapid Miner tool has been discussed. ...
The term data mining is not appropriate for the process knowledge mining or knowledge discovery would be more suitable because knowledge is to be extracted by this process from large data sets. ...
doi:10.35940/ijdm.b1615.111221
fatcat:2qcco3l5uvcchdujpby7cdv4q4
Modular Implementation of Neural network Structures in Marketing Domain Using Data Mining Technique
2016
International Journal Of Engineering And Computer Science
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The real time implementation of marketing domain is taken into account for the incorporation of data mining rule extraction approach which involves extracting symbolic models from trained neural networks ...
The proper application of supervised and unsupervised learning concepts play a vital role in the successful implementation of Neural network methods which are not easily used for data mining tasks. ...
Specifically we describe algorithms that are able to extract symbolic rules from trained neural networks and algorithms that are able to directly learn comprehensible models .Inductive learning is a central ...
doi:10.18535/ijecs/v5i1.28
fatcat:y4gycctl7femjdzpz4p33jz4j4
A SURVEY ON TOOLS USED FOR MACHINE LEARNING
2020
International Journal of Engineering Applied Sciences and Technology
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Machine Learning is applied in different applications such as Agriculture, Data Quality, Information Retrieval, Financial Market Analysis etc.., In this paper, we have discussed few tools like Scikit learn ...
In this paper, a brief introduction to Machine Learning and its Tools are studied. In the recent developments, most of the Machine learning tools are more advanced and efficient. ...
It is used for building neural
networks through autograde
module.
It provides optimization algorithms
for neural networks
Neural Network
It helps in creating
computational graphs. ...
doi:10.33564/ijeast.2020.v04i09.012
fatcat:25vej5lc7zbkne3a357qagrsoy
A Brief survey of Data Mining Techniques Applied to Agricultural Data
2014
International Journal of Computer Applications
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In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. ...
The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy cmeans. ...
The goal of the data mining process is to extract knowledge from an existing data set and transform it into a human understandable formation for advance use. ...
doi:10.5120/16620-6472
fatcat:cpkksfmknrbuhmoflksqxhofbi
A Review of Unsupervised Artificial Neural Networks with Applications
2019
International Journal of Computer Applications
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Artificial Neural Networks (ANNs) are models formulated to mimic the learning capability of human brains. Learning in ANNs can be categorized into supervised, reinforcement and unsupervised learning. ...
Unsupervised algorithms have become very useful tools in segmentation of Magnetic resonance images for detection of anomalies in the body systems. ...
Selforganizing maps are artificial neural network algorithms for data mining [29] . Massive data can be analysed and visualized efficiently by self-organising maps [25] . ...
doi:10.5120/ijca2019918425
fatcat:v3u4m3d24bdflkq7vryiya66mu
A REVIEW ON DIFFERENT COMPUTING METHOD FOR BREAST CANCER DIAGNOSIS USING ARTIFICIAL NEURAL NETWORK AND DATAMINING TECHNIQUES
2016
International Journal of Advanced Research
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This paper presented a model Adaptive Resonance Neural Networks (ARNN) specially ART2 models of neural networks for clustering. ...
The second category of "unsupervised learning technique" is assigned to the tasks of clustering and association rules mining. ...
doi:10.21474/ijar01/2123
fatcat:4vyz65gw3jc4bkgyaqfw4llz4u
Review on Classification and Clustering using Fuzzy Neural Networks
2016
International Journal of Computer Applications
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In data mining two important tasks involved are classification and clustering. ...
In recent past many fuzzy neural networks have been proposed which can be employed for classification and clustering. ...
A rule extraction algorithm is also included. Fuzzy if-then rules are extracted after pruning the network. ...
doi:10.5120/ijca2016908456
fatcat:t5ihmtoz2vda5m6do6jma6eizy
A REVIEW ON ROAD ACCIDENT DETECTION USING DATA MINING TECHNIQUES
2018
International Journal of Advanced Research in Computer Science
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Data mining techniques are widely used for road accident detection. ...
Despite improvement in technology, there has been increase in the rate of accidents. A large number of precious lives are lost because of road traffic accidents every day. ...
These features were fed into a regression tree, neighbor model and feed forward neural network model. ...
doi:10.26483/ijarcs.v9i2.5708
fatcat:dt3qbinxwvh6ldmp57z6nwdvky
A Classification Technique using Associative Classification
2011
International Journal of Computer Applications
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Classification and association rule mining are two basic tasks of Data Mining. Classification rule mining is used to discover a small set of rules in the database to form an accurate classifier. ...
Association rules mining has been used to reveal all interesting relationships in a potentially large database. ...
extraction [1] : Clustering (also called segmentation or unsupervised learning), Predictive modeling (also called classification or supervised learning), and Frequent pattern extraction. ...
doi:10.5120/2430-3268
fatcat:koaysp253re6tjwek4zorysnge
Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction
2011
International Journal of Computer Applications
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classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. ...
This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today"s medical research particularly in Heart ...
Performance Study of Algorithm
Data Mining and Artificial Neural network Intelligent Heart Disease Prediction System (IHDPS) using data mining techniques, namely, Decision Trees, Naïve Bayes and Neural ...
doi:10.5120/2237-2860
fatcat:bnwurqgxejcqbcmb5qhnho7j6i
Research on Data Mining Using Neural Networks
2013
International Journal of Computer Science and Informatics
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The application of neural networks in the data mining has become wider. ...
In this paper, the data mining based on neural networks is researched in detail, and the key technology and ways to achieve the data mining based on neural networks are also researched. ...
from recursive network, the algorithm of binary input and output rules extracting (BIO-RE), partial rules extracting algorithm (Partial-RE) and full rules extracting algorithm (Full-RE). ...
doi:10.47893/ijcsi.2013.1085
fatcat:gifdwx4clrhepolhnjcbfbnsfq
Data Mining and Its Applications in Higher Education
2002
New Directions for Institutional Research
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According to Rubenking (2001) , "data mining is a logical evolution in database technology. ...
Thearling (1995) even chronicled the evolution of data as data collection in the 1960s, data access in the 1980s, data navigation in the 1990s, and data mining in the new century. ...
As neural networks results were a bit cryptic, it was necessary to use a rule induction model to list the rules uncovered. The following resulted from C5.0: Model Analysis. ...
doi:10.1002/ir.35
fatcat:76rnyuvarrfvldmzbtazlhs3qu
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