Papers by Vijayarani Mohan
... Private Information in Privacy Preserving Data Mining S.Vijayarani #1 , Dr.A.Tamilarasi *2 #1... more ... Private Information in Privacy Preserving Data Mining S.Vijayarani #1 , Dr.A.Tamilarasi *2 #1 Assistant Professor, School of Computer Science and Engineering, Bharathiar University, Coimbatore *2 Prof&Head, Department of MCA, Kongu Engg. College, Erode ...
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INTERNATIONAL JOURNAL OF COMPUTING ALGORITHM, 2015
Traffic control method is an interconnection of sign devices positioned at road intersections, pe... more Traffic control method is an interconnection of sign devices positioned at road intersections, pedestrian crossings and different locations to manage competing flows of traffic. This work provides a novel idea and application procedures of priority and round robin scheduling algorithms. These algorithms methodologies are combined and a new hybrid automatic traffic control mechanism is proposed for efficient traffic and transportation management systems. This mechanism is necessary and it may be applied to modern cities wherever particular paths have more traffic jam compared to other paths of signal and wherever equal or normal traffic. This proposed hybrid approach applies priority scheduling concept to specific paths which has more traffic, on the other hand, this hybrid approach uses round robin scheduling concept for normal traffic signals. This mechanism is well suited for controlling the traffic of modern and old cities, both preplanned and not preplanned before construction. Some general conclusions and promising future research topics are also provided.
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International Journal
... Data Mining. Author: S.Vijayarani, Dr.A.Tamilarasi. Abstract: Privacy Preserving Data Mining ... more ... Data Mining. Author: S.Vijayarani, Dr.A.Tamilarasi. Abstract: Privacy Preserving Data Mining has become very popular for protecting the confidential knowledge which was extracted from the data mining techniques. Privacy preserving ...
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Advances in Business Information Systems and Analytics, 2019
The rapid development of online social media is the method of collaboratively produced content ma... more The rapid development of online social media is the method of collaboratively produced content material presents new possibilities and challenges to both producers and patrons of knowledge. The term big data refers to large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques. In the current scenario, social media has gained amazing attention within the last decade. Accessing social media platforms and websites such as Facebook, Twitter, YouTube, LinkedIn, Instagram, and Google+, web technologies have become more responsible. People are becoming more fascinated about and relying on social media platform for records, news, and opinion of other customers on diverse topics. Hence, these situations produce a large volume of data. The main objective of this chapter is to provide knowledge about big data analytics in social media. A brief overview of big data and social media are discussed. Research challenges in so...
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International Journal of Intelligent Enterprise
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International Journal of Database Management Systems, 2010
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Journal of Digital Information Management, 2018
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Advanced Computing: An International Journal (ACIJ), 2009
Data mining is the process of extracting patterns from data. Data mining is seen as an increasing... more Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. Data Mining can be utilized in any organization that needs to find patterns or relationships in their data. A group of techniques that find relationships that have not previously been discovered. In many situations, the extracted patterns are highly private and it should not be disclosed. In order to maintain the secrecy of data, there is in need of several techniques and algorithms for modifying the original data in order to limit the extraction of confidential patterns. There have been two types of privacy in data mining. The first type of privacy is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy is that the data is manipulated so that the mining result is not affected or minimally affected. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on data bases without violating the privacy of individuals. Many techniques for privacy preserving data mining have come up over the last decade. Some of them are statistical, cryptographic, randomization methods, k-anonymity model, l-diversity and etc. In this work, we propose a new perturbative masking technique known as data transformation technique can be used for protecting the sensitive information. An experimental result shows that the proposed technique gives the better result compared with the existing technique.
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International Journal of Swarm Intelligence Research
Association rule mining is an important and widely used data mining technique. It is used to retr... more Association rule mining is an important and widely used data mining technique. It is used to retrieve highly related objects in a database based on the occurrence. Recently, utility-based association rules were proposed to consider significant factors of the object. The main objective of this research work is to retrieve high utility association rules from a database using cockroach swarm optimization algorithm. So far, in the literature, no optimization algorithm was proposed in utility-based association rule mining. In this research work, CSOUAR (cockroach swarm optimization for high utility association rule mining) algorithm was proposed to generate utility association rules. CSOUAR algorithm is based on three behaviours of cockroach: chase-swarming, dispersing, and ruthless. To analyse the performance of CSOUAR, an improved particle swarm optimization (PSO-UAR), animal migration optimization (AMO-UAR), bees swarm optimisation (BSO-UAR), and penguins search optimisation (peSO-UAR...
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Information Discovery and Delivery
Purpose Owing to the huge volume of documents available on the internet, text classification beco... more Purpose Owing to the huge volume of documents available on the internet, text classification becomes a necessary task to handle these documents. To achieve optimal text classification results, feature selection, an important stage, is used to curtail the dimensionality of text documents by choosing suitable features. The main purpose of this research work is to classify the personal computer documents based on their content. Design/methodology/approach This paper proposes a new algorithm for feature selection based on artificial bee colony (ABCFS) to enhance the text classification accuracy. The proposed algorithm (ABCFS) is scrutinized with the real and benchmark data sets, which is contrary to the other existing feature selection approaches such as information gain and χ2 statistic. To justify the efficiency of the proposed algorithm, the support vector machine (SVM) and improved SVM classifier are used in this paper. Findings The experiment was conducted on real and benchmark dat...
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International Journal of Information Security and Privacy
This article describes how privacy preserving data mining has become one of the most important an... more This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding re...
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Integrated Intelligent Research, 2012
Traffic control method is an interconnection of sign devices positioned at road intersections, pe... more Traffic control method is an interconnection of sign devices positioned at road intersections, pedestrian crossings and different locations to manage competing flows of traffic. This work provides a novel idea and application procedures of priority and round robin scheduling algorithms. These algorithms methodologies are combined and a new hybrid automatic traffic control mechanism is proposed for efficient traffic and transportation management systems. This mechanism is necessary and it may be applied to modern cities wherever particular paths have more traffic jam compared to other paths of signal and wherever equal or normal traffic. This proposed hybrid approach applies priority scheduling concept to specific paths which has more traffic, on the other hand, this hybrid approach uses round robin scheduling concept for normal traffic signals. This mechanism is well suited for controlling the traffic of modern and old cities,both preplanned and not preplanned before construction. S...
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The main objective of this research work isto find the keywords in the captured/scanned pr... more The main objective of this research work isto find the keywords in the captured/scanned printdocument images in the image database. Documentimages are becoming more popular in today’s world andthese are used in paperless offices and digital libraries.Information retrieval from the document images is a verychallenging task. Hence, there is a need for developingsearching strategies to find the required information fromthese document images as per user’s needs, becomesvery essential in nowadays. Traditionally Optical CharacterRecognition (OCR) tools are used for information retrievalfrom the document images, but it’s not an efficient method.Word spotting is an inventive method for searching thedocument images and to retrieve relevant informationwithout any conversion. In this work an algorithm EnhancedDynamic Time Warping was proposed to for findingkeywords from document images, it is based on wordspotting technique. Different matching algorithms are madeavailable for word spotting. Popular algorithms areNormalization Cross Correlation (NCC) and Dynamic TimeWarping (DTW). In this work, we have compared theperformance of these two existing algorithms with theproposed algorithm named as Enhanced Dynamic TimeWarping algorithm (EDTW). Different image formats anddifferent sizes of images are used for experimentation.From the results it is observed that the proposed algorithmhas produced good results than an existing one.
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International Journal of Knowledge Based Computer Systems , 2016
Cluster analysis is an unsupervised learning approach that aims to group the objects into differe... more Cluster analysis is an unsupervised learning approach that aims to group the objects into different groups or clusters. So that
each cluster can contain similar objects with respect to any predefined condition. Text document clustering is the important technique of text
mining in efficiently organizing the large volume of documents into a small number of significant clusters. The main objective of this research
work is to cluster the collection of documents into related groups based on the contents of the particular documents. In order to perform this
clustering task, this research work makes use of two existing algorithms, namely K-means and Bisecting K-means algorithm, and also this
research work proposes a new clustering algorithm namely Enhanced-Bisecting K-means algorithm. From the experimental results it is
observed that the proposed algorithm gives the better clustering accuracy than other algorithms.
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Journal of Applied Information Science, 2016
Utility mining is an emerging topic in data mining. The
aim of utility mining is to discover the ... more Utility mining is an emerging topic in data mining. The
aim of utility mining is to discover the itemsets that have
maximum utilities. Here utility refers number of items
bought, cost of an item or it can be any other user choice
in a transaction database. Frequent itemset mining
is starting point of utility mining. In frequent itemset
mining most often occurring itemsets in a transaction
are retrieved. The discovery of such frequent itemsets
can help in many business decision making process.
Frequent itemset mining concentrates on the number
of occurrence of items in a transaction, but not the
value of items. But utility mining considers importance
of itemsets like the profit it earns in a transaction,
quantity in a transaction. In this paper various utility
mining algorithms like MEU (Mining with expected
utility), FUM (Fast Utility Mining), Two-Phase, CTUMine,
UP-Growth (Utility Pattern Growth), and FHM
(Faster High Utility itemset Mining) MHUI-BIT (Mining
High-Utility Itemsets based on BIT vector), MHUT-TID
(Mining High-Utility Itemsets based on TIDlist), and
THUI (Temporal High Utility Itemsets) are discussed.
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Papers by Vijayarani Mohan
each cluster can contain similar objects with respect to any predefined condition. Text document clustering is the important technique of text
mining in efficiently organizing the large volume of documents into a small number of significant clusters. The main objective of this research
work is to cluster the collection of documents into related groups based on the contents of the particular documents. In order to perform this
clustering task, this research work makes use of two existing algorithms, namely K-means and Bisecting K-means algorithm, and also this
research work proposes a new clustering algorithm namely Enhanced-Bisecting K-means algorithm. From the experimental results it is
observed that the proposed algorithm gives the better clustering accuracy than other algorithms.
aim of utility mining is to discover the itemsets that have
maximum utilities. Here utility refers number of items
bought, cost of an item or it can be any other user choice
in a transaction database. Frequent itemset mining
is starting point of utility mining. In frequent itemset
mining most often occurring itemsets in a transaction
are retrieved. The discovery of such frequent itemsets
can help in many business decision making process.
Frequent itemset mining concentrates on the number
of occurrence of items in a transaction, but not the
value of items. But utility mining considers importance
of itemsets like the profit it earns in a transaction,
quantity in a transaction. In this paper various utility
mining algorithms like MEU (Mining with expected
utility), FUM (Fast Utility Mining), Two-Phase, CTUMine,
UP-Growth (Utility Pattern Growth), and FHM
(Faster High Utility itemset Mining) MHUI-BIT (Mining
High-Utility Itemsets based on BIT vector), MHUT-TID
(Mining High-Utility Itemsets based on TIDlist), and
THUI (Temporal High Utility Itemsets) are discussed.
each cluster can contain similar objects with respect to any predefined condition. Text document clustering is the important technique of text
mining in efficiently organizing the large volume of documents into a small number of significant clusters. The main objective of this research
work is to cluster the collection of documents into related groups based on the contents of the particular documents. In order to perform this
clustering task, this research work makes use of two existing algorithms, namely K-means and Bisecting K-means algorithm, and also this
research work proposes a new clustering algorithm namely Enhanced-Bisecting K-means algorithm. From the experimental results it is
observed that the proposed algorithm gives the better clustering accuracy than other algorithms.
aim of utility mining is to discover the itemsets that have
maximum utilities. Here utility refers number of items
bought, cost of an item or it can be any other user choice
in a transaction database. Frequent itemset mining
is starting point of utility mining. In frequent itemset
mining most often occurring itemsets in a transaction
are retrieved. The discovery of such frequent itemsets
can help in many business decision making process.
Frequent itemset mining concentrates on the number
of occurrence of items in a transaction, but not the
value of items. But utility mining considers importance
of itemsets like the profit it earns in a transaction,
quantity in a transaction. In this paper various utility
mining algorithms like MEU (Mining with expected
utility), FUM (Fast Utility Mining), Two-Phase, CTUMine,
UP-Growth (Utility Pattern Growth), and FHM
(Faster High Utility itemset Mining) MHUI-BIT (Mining
High-Utility Itemsets based on BIT vector), MHUT-TID
(Mining High-Utility Itemsets based on TIDlist), and
THUI (Temporal High Utility Itemsets) are discussed.