Clustering Techniques in Data Mining—A Survey

Pujari, Arun K.; Rajesh, K S; Reddy, D Suresh

doi:10.1080/03772063.2001.11416199

Cited by 32 publications

(32 citation statements)

References 6 publications

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“…The major challenge is to infer valuable information from the observed data that may have unexplored potential to improve the efficiency of the related sector, which lead to the emergence of techniques of Data Mining that find wide application in agriculture and allied sectors. The major focus and intension of the technique is to use existing data to invent new facts and to uncover new relationships previously unknown even to the experts [21]. In Agriculture, Data Mining techniques have been recently used for recognizing and grading fruits [22].…”

Section: Ability To Learnmentioning

confidence: 99%

Intelligence and its Application in Agriculture: Techniques to Deal with Variations and Uncertainties

Bhagawati¹,

Bhagawati²,

Jini³

2016

IJISA

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Abstract-Biological systems, including agriculture and allied sectors are very complex and nonlinear in nature. The pace of current climate change, which is unique about it, makes the biological system more and more complicated and unpredictable. The novelty or ambiguity that the variable environment presents, demands for the development of self-adaptive intelligent systems in agriculture and allied sectors. Agriculture is emerging as knowledge-based enterprise that demands efficient needbased information retrieval systems and smart actions. Intelligence is that resource that guides actions and provide options under variable, uncertain and unseen conditions. The objective of the current paper is to analyze the attributes that are considered to be characteristics of intelligence having wide potential for the development of intelligent system and technologies for agricultural applications. The intelligent techniques like forecasting, database management, knowledge discovery, deception, simulation, contingency planning etc. revolutionize the whole agricultural sector opening new and competent options and dimensions. Sustainable agricultural development demands multidisciplinary holistic approach and intelligence should be the guiding principle that demands study of human cognitive psychology.

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Section: Ability To Learnmentioning

confidence: 99%

Intelligence and its Application in Agriculture: Techniques to Deal with Variations and Uncertainties

Bhagawati¹,

Bhagawati²,

Jini³

2016

IJISA

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“…In paper [9,11], one more phase in the form of mutation operator of genetic algorithm is added to original Artificial Bee Colony algorithm. In standard ABC algorithm, there are only 4 phases that described the overall working of this algorithm, but here one additional phase after the onlooker bee phase of ABC algorithm is added in the form of Mutation operator.…”

Section: Artifical Bee Colony Algorithm With Mutationmentioning

confidence: 99%

“…In this scenario, data mining [2,9] plays an important rule for mining data according to the customer's requirements. Association rule mining is one of the popular and well known research methods for discovering interesting relationships between variables in large information repository or databases.…”

Section: Introductionmentioning

confidence: 99%

Association Rules Optimization using Artificial Bee Colony Algorithm with Mutation

Sharma¹,

Tiwari²,

Gupta³

2015

IJCA

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In data mining, Association rule mining is one of the popular and simple method to find the frequent item sets from a large dataset. While generating frequent item sets from a large dataset using association rule mining, computer takes too much time. This can be improved by using artificial bee colony algorithm (ABC). The Artificial bee colony algorithm is an optimization algorithm based on the foraging behavior of artificial honey bees. In this paper, artificial bee colony algorithm with mutation operator is used to generate high quality association rules for finding frequent item sets from large data sets. The mutation operator is used after the scout bee phase in this work. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using artificial bee colony algorithm (ABC) over these rules the system can predict the rules which contains negative attributes.

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“…CART (Classification and Regression Tree) [2] is one of the popular methods of building decision tree in the machine learning community. CART builds a binary decision tree by splitting the record at each node, according to a function of a single attribute.…”

Section: Classification and Regression Tree (Cart)mentioning

confidence: 99%

An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set

Shrivas¹,

Dewangan²

2014

IJCA

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Information security is extremely critical issues for every organization to protect information from unauthorized access. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviours. Basically Intrusion detection system is a classifier that can classify the data as normal or attacks. In this research paper, we have proposed ANN-Bayesian Net-GR technique that means ensemble of Artificial Neural Network (ANN) and Bayesian Net with Gain Ratio (GR) feature selection technique. We have applied various individual classification techniques and its ensemble model on KDD99 and NSL-KDD data set to check the robustness of model. Due to irrelevant features in data set, also applied Gain Ratio feature selection technique on best model. Finally our proposed model produces highest accuracy compare to others.

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Clustering Techniques in Data Mining—A Survey

Cited by 32 publications

References 6 publications

Intelligence and its Application in Agriculture: Techniques to Deal with Variations and Uncertainties

Intelligence and its Application in Agriculture: Techniques to Deal with Variations and Uncertainties

Association Rules Optimization using Artificial Bee Colony Algorithm with Mutation

An Ensemble Model for Classification of Attacks with Feature Selection based on KDD99 and NSL-KDD Data Set

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