Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496472
S. Kasthuri, T. Meyyappan
Data mining is an essential technology to extract patterns or knowledge from large repositories of data. Association rules in market basket database represent the shopping behavior of customers. The association information may reveal trade secrets. It must be hidden before publishing. Association rule hiding in privacy preserving data mining hides sensitive rules containing sensitive items. In this paper, a new method is proposed to detect the sensitive items for hiding sensitive association rules. This proposed method finds the frequent item sets and generates the association rules. It employs the concept of representative association rules to detect sensitive items.
{"title":"Detection of sensitive items in market basket database using association rule mining for privacy preserving","authors":"S. Kasthuri, T. Meyyappan","doi":"10.1109/ICPRIME.2013.6496472","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496472","url":null,"abstract":"Data mining is an essential technology to extract patterns or knowledge from large repositories of data. Association rules in market basket database represent the shopping behavior of customers. The association information may reveal trade secrets. It must be hidden before publishing. Association rule hiding in privacy preserving data mining hides sensitive rules containing sensitive items. In this paper, a new method is proposed to detect the sensitive items for hiding sensitive association rules. This proposed method finds the frequent item sets and generates the association rules. It employs the concept of representative association rules to detect sensitive items.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129800168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496453
M. Kannika Nirai Vaani, E. Ramaraj
Association rule mining is one of the most important and well-researched techniques of data mining, that aims to induce associations among sets of items in transaction databases or other data repositories. Currently Apriori algorithms play a major role in identifying frequent item set and deriving rule sets out of it. But it uses the conjunctive nature of association rules, and the single minimum support factor to generate the effective rules. However the above two factors are alone not adequate to derive useful rules effectively. Hence in the proposed algorithm we have taken Apriori Algorithm as a reference and included disjunctive rules and multiple minimum supports also to capture all possible useful rules. Although few algorithms [4] [5] are dealing the disjunctive rules and multiple minimum supports separately to some extent, the proposed concept is to integrate all into one that lead to a robust algorithm. And the salient feature of our work is introducing Genetic Algorithm (GA) in deriving possible Association Rules from the frequent item set in an optimized manner. Besides we have taken one more add-on factor `Lift Ratio' which is to validate the generated Association rules are strong enough to infer useful information. Hence this new approach aims to put together the above points to generate an efficient algorithm with appropriate modification in Apriori Algorithm so that to offer interesting/useful rules in an effective and optimized manner with the help of Genetic Algorithm.
{"title":"An integrated approach to derive effective rules from association rule mining using genetic algorithm","authors":"M. Kannika Nirai Vaani, E. Ramaraj","doi":"10.1109/ICPRIME.2013.6496453","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496453","url":null,"abstract":"Association rule mining is one of the most important and well-researched techniques of data mining, that aims to induce associations among sets of items in transaction databases or other data repositories. Currently Apriori algorithms play a major role in identifying frequent item set and deriving rule sets out of it. But it uses the conjunctive nature of association rules, and the single minimum support factor to generate the effective rules. However the above two factors are alone not adequate to derive useful rules effectively. Hence in the proposed algorithm we have taken Apriori Algorithm as a reference and included disjunctive rules and multiple minimum supports also to capture all possible useful rules. Although few algorithms [4] [5] are dealing the disjunctive rules and multiple minimum supports separately to some extent, the proposed concept is to integrate all into one that lead to a robust algorithm. And the salient feature of our work is introducing Genetic Algorithm (GA) in deriving possible Association Rules from the frequent item set in an optimized manner. Besides we have taken one more add-on factor `Lift Ratio' which is to validate the generated Association rules are strong enough to infer useful information. Hence this new approach aims to put together the above points to generate an efficient algorithm with appropriate modification in Apriori Algorithm so that to offer interesting/useful rules in an effective and optimized manner with the help of Genetic Algorithm.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116578116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496512
K. Mathan, T. Ravichandran
This paper presents a low-power ternary content adressable memory (TCAM) design, in which we propose refined search enable (RSE scheme that aims to reduce the TCAM power dissipated in the search-line (SL) switching activity. By exploiting the vertically continuous “don't-care” feature, the DCG scheme can effectively reduce the average SL power consumption per switch. The refined search enables (RSE) technique to eliminate the unnecessary SL switching activity in the quiet pattern. By reducing both the SL switching activity and the average switching power, the proposed design can minimize the TCAM SL power consumption. For a 128 32 TCAM, the best configuration we examined shows that when the gating granularity is 16, with a 1.3% search performance improvement, The RSE technique can achieve 72% 79% SL energy reduction.
{"title":"Refined search enable (RSE) TCAM design used in network routing table","authors":"K. Mathan, T. Ravichandran","doi":"10.1109/ICPRIME.2013.6496512","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496512","url":null,"abstract":"This paper presents a low-power ternary content adressable memory (TCAM) design, in which we propose refined search enable (RSE scheme that aims to reduce the TCAM power dissipated in the search-line (SL) switching activity. By exploiting the vertically continuous “don't-care” feature, the DCG scheme can effectively reduce the average SL power consumption per switch. The refined search enables (RSE) technique to eliminate the unnecessary SL switching activity in the quiet pattern. By reducing both the SL switching activity and the average switching power, the proposed design can minimize the TCAM SL power consumption. For a 128 32 TCAM, the best configuration we examined shows that when the gating granularity is 16, with a 1.3% search performance improvement, The RSE technique can achieve 72% 79% SL energy reduction.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131381441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496480
V. Annapoorani, A. Vijaya
Spontaneous sorting clock hoop around two significant concepts of sorting and summarizing data. While sorting is the primary concern of this tool, summarization is its secondary concern. As its name itself signifies, the primary concern sorting, the tool sorts the data simultaneously into various groups based on the title. It constructs an index in a clockwise manner, which makes it simpler and easier for the researches in searching for the required data. Since, the sorting is done in clock-based form, where the starting point collides with the ending point; likewise the earliest data meets the rearmost. So, the data search is performed in both forward and backward directions which in turn doubles up the speed of the same process done only in the forward direction.
{"title":"A prevailing judicial package for clustering and sorting information extraction","authors":"V. Annapoorani, A. Vijaya","doi":"10.1109/ICPRIME.2013.6496480","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496480","url":null,"abstract":"Spontaneous sorting clock hoop around two significant concepts of sorting and summarizing data. While sorting is the primary concern of this tool, summarization is its secondary concern. As its name itself signifies, the primary concern sorting, the tool sorts the data simultaneously into various groups based on the title. It constructs an index in a clockwise manner, which makes it simpler and easier for the researches in searching for the required data. Since, the sorting is done in clock-based form, where the starting point collides with the ending point; likewise the earliest data meets the rearmost. So, the data search is performed in both forward and backward directions which in turn doubles up the speed of the same process done only in the forward direction.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132517798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496489
Sumaiya Thaseen, C. Kumar
Due to increase in intrusion incidents over internet, many network intrusion detection systems are developed to prevent network attacks. Data mining, pattern recognition and classification methods are used to classify network events as a normal or anomalous one. This paper is aimed at evaluating different tree based classification algorithms that classify network events in intrusion detection systems. Experiments are conducted on NSL-KDD 99 dataset. Dimensionality of the attribute of the dataset is reduced. The results show that RandomTree model holds the highest degree of accuracy and reduced false alarm rate. RandomTree model is evaluated with other leading intrusion detection models to determine its better predictive accuracy.
{"title":"An analysis of supervised tree based classifiers for intrusion detection system","authors":"Sumaiya Thaseen, C. Kumar","doi":"10.1109/ICPRIME.2013.6496489","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496489","url":null,"abstract":"Due to increase in intrusion incidents over internet, many network intrusion detection systems are developed to prevent network attacks. Data mining, pattern recognition and classification methods are used to classify network events as a normal or anomalous one. This paper is aimed at evaluating different tree based classification algorithms that classify network events in intrusion detection systems. Experiments are conducted on NSL-KDD 99 dataset. Dimensionality of the attribute of the dataset is reduced. The results show that RandomTree model holds the highest degree of accuracy and reduced false alarm rate. RandomTree model is evaluated with other leading intrusion detection models to determine its better predictive accuracy.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133279659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496483
P. Nagarjun, V. A. Kumar, C. Kumar, A. Ravi
Denial-of-Service attacks (DoS) have become a widespread problem on the Internet. These attacks are easy to execute. Low rate attacks are relatively new variants of DoS attacks. Low rate DoS attacks are difficult to detect since attacker sends attack stream with low volume and the countermeasures used to handle the high rate DoS attacks are not suitable for these types of attacks. RTS/CTS attack is one type of Low rate DoS attack. In this paper, we analyze RTS/CTS attack which exploits the medium reservation mechanism of 802.11 networks through duration field. We propose variants of RTS/CTS attacks in wireless networks. We simulate the attacks behaviour in ns2 simulation environment to demonstrate the attack feasibility as well as potential negative impact of these attacks on 802.11 based networks. We have created an application that has the capability to create test bed environment for the attacks, perform RTS/CTS attacks and generate suitable graphs to analyze the attack's behaviour. We also briefly discuss possible ways of detecting and mitigating such Low rate DoS attacks in wireless networks.
{"title":"Simulation and analysis of RTS/CTS DoS attack variants in 802.11 networks","authors":"P. Nagarjun, V. A. Kumar, C. Kumar, A. Ravi","doi":"10.1109/ICPRIME.2013.6496483","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496483","url":null,"abstract":"Denial-of-Service attacks (DoS) have become a widespread problem on the Internet. These attacks are easy to execute. Low rate attacks are relatively new variants of DoS attacks. Low rate DoS attacks are difficult to detect since attacker sends attack stream with low volume and the countermeasures used to handle the high rate DoS attacks are not suitable for these types of attacks. RTS/CTS attack is one type of Low rate DoS attack. In this paper, we analyze RTS/CTS attack which exploits the medium reservation mechanism of 802.11 networks through duration field. We propose variants of RTS/CTS attacks in wireless networks. We simulate the attacks behaviour in ns2 simulation environment to demonstrate the attack feasibility as well as potential negative impact of these attacks on 802.11 based networks. We have created an application that has the capability to create test bed environment for the attacks, perform RTS/CTS attacks and generate suitable graphs to analyze the attack's behaviour. We also briefly discuss possible ways of detecting and mitigating such Low rate DoS attacks in wireless networks.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133830364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496724
S. S. Kumar, H. Inbarani
Tagging is a popular way to annotate web 2.0 web sites. A tag is any user-generated word or phrase that helps to organize web 2.0 content. The current hype around web 2.0 applications, poses several important challenges for future data and web mining methods. An important challenge of Web 2.0 is the fact that a large amount of data has been generated over a short period. Clustering the tag data is very tedious since the tag space is very large in several social book marking web sites. So, instead of clustering the whole tag space of Web 2.0 data, some tags frequent enough in the tag space can be selected for clustering by applying feature selection techniques. The goal of feature selection is to determine a marginal bookmarked URL subset from a Web 2.0 data while retaining a suitably high accuracy in representing the original bookmarks. Tag clustering is the process of grouping similar tags into the same cluster and is important for the success of collaborative tagging services. In this paper, Unsupervised Quick Reduct feature selection algorithm is applied to find a set of most commonly tagged bookmarks and then clustering techniques such as Soft rough fuzzy clustering and Rough K-Means algorithms are applied for clustering of user generated tags and the performance of these clustering approaches are illustrated in this paper.
{"title":"Web 2.0 social bookmark selection for tag clustering","authors":"S. S. Kumar, H. Inbarani","doi":"10.1109/ICPRIME.2013.6496724","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496724","url":null,"abstract":"Tagging is a popular way to annotate web 2.0 web sites. A tag is any user-generated word or phrase that helps to organize web 2.0 content. The current hype around web 2.0 applications, poses several important challenges for future data and web mining methods. An important challenge of Web 2.0 is the fact that a large amount of data has been generated over a short period. Clustering the tag data is very tedious since the tag space is very large in several social book marking web sites. So, instead of clustering the whole tag space of Web 2.0 data, some tags frequent enough in the tag space can be selected for clustering by applying feature selection techniques. The goal of feature selection is to determine a marginal bookmarked URL subset from a Web 2.0 data while retaining a suitably high accuracy in representing the original bookmarks. Tag clustering is the process of grouping similar tags into the same cluster and is important for the success of collaborative tagging services. In this paper, Unsupervised Quick Reduct feature selection algorithm is applied to find a set of most commonly tagged bookmarks and then clustering techniques such as Soft rough fuzzy clustering and Rough K-Means algorithms are applied for clustering of user generated tags and the performance of these clustering approaches are illustrated in this paper.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133738504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496517
S. Subbaiah
Text mining is a process of extracting knowledge from large text documents. A new probabilistic classifier for text mining is proposed in this paper. It uses ODP taxonomy and domain ontology and datasets to cluster and identify the category of the given text document. The proposed work has three steps, namely, preprocessing, rule generation and probability calculation. At the stage of preprocessing the input document is split into paragraphs and statements. In rule generation, the documents from the training set are read. In probability calculation, positive and negative weight factor is calculated. The proposed algorithm calculates the positive probability value and negative probability value for each term set or pattern identified from the document. Based on the calculated probability value the probabilistic classifier indexes the document to the concern group of the cluster.
{"title":"Extracting knowledge using probabilistic classifier for text mining","authors":"S. Subbaiah","doi":"10.1109/ICPRIME.2013.6496517","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496517","url":null,"abstract":"Text mining is a process of extracting knowledge from large text documents. A new probabilistic classifier for text mining is proposed in this paper. It uses ODP taxonomy and domain ontology and datasets to cluster and identify the category of the given text document. The proposed work has three steps, namely, preprocessing, rule generation and probability calculation. At the stage of preprocessing the input document is split into paragraphs and statements. In rule generation, the documents from the training set are read. In probability calculation, positive and negative weight factor is calculated. The proposed algorithm calculates the positive probability value and negative probability value for each term set or pattern identified from the document. Based on the calculated probability value the probabilistic classifier indexes the document to the concern group of the cluster.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496456
S. Velmurugan, C. Rajasekaran
The data acquisition and processing architecture covers the most demanding applications in continuous patient monitoring for chronic diseases in medical field. The multichannel data acquisition is essential for acquiring and monitoring the various biomedical signals from biomedical sensors or signals from industrial sensors. The problem is that the data storage and hardware size, so the multichannel data obtained is processed at runtime and stored in an external storage for future reference. The method of implementing the proposed design is the system on-chip via field programmable gate array (SoC-FPGA) to reduce the hardware size and for memory size. The Soc-FPGA attains high resolution and real time processing of data acquisition and signal processing. A four channel data acquisition and processing (DAQP) was designed, developed using the Lab VIEW graphical programming. NI DAQ and NI FPGA module is used to test and implement the design for real time. The module was designed inorder to provide high accuracy, storage and portability.
{"title":"A reconfigurable on-chip multichannel data acquisition and processing (DAQP) system for multichannel signal processing","authors":"S. Velmurugan, C. Rajasekaran","doi":"10.1109/ICPRIME.2013.6496456","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496456","url":null,"abstract":"The data acquisition and processing architecture covers the most demanding applications in continuous patient monitoring for chronic diseases in medical field. The multichannel data acquisition is essential for acquiring and monitoring the various biomedical signals from biomedical sensors or signals from industrial sensors. The problem is that the data storage and hardware size, so the multichannel data obtained is processed at runtime and stored in an external storage for future reference. The method of implementing the proposed design is the system on-chip via field programmable gate array (SoC-FPGA) to reduce the hardware size and for memory size. The Soc-FPGA attains high resolution and real time processing of data acquisition and signal processing. A four channel data acquisition and processing (DAQP) was designed, developed using the Lab VIEW graphical programming. NI DAQ and NI FPGA module is used to test and implement the design for real time. The module was designed inorder to provide high accuracy, storage and portability.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133438511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-04-15DOI: 10.1109/ICPRIME.2013.6496491
V. Suriyakumari, A. V. Kathiravan
Performance evaluation in virtual organizations is one of the most important issues that have been considered due to the transition from industrial age to knowledge era. Virtual organizations, as one of the challenges of third millennium, which came to existence for enhancing organization's performance through outsourcing, are not excluding. A virtual organization and its smaller variant, the virtual team, is an organizational network that is structured and managed to function as an identifiable and complete organization. Determining what meanings virtual team members attach to performance evaluation system in IT Companies is a vital precursor to understand the effectiveness of the management practice, rendering this study a preliminary investigation. The literature confirms that perceptions of management practices in IT Industries can influence employee loyalty and role-related behaviors. Perceptions of unfairness can be more detrimental for geographically distributed workers in MNCs than for collocated teams. Although businesses continue to drive demands for virtual organizations, most contemporary studies of performance evaluation system are limited to traditional organizational settings. An interpretive, phenomenological domain Driven Data Mining (D3M) approach utilizing 360 Degree data mining for objective measurement and opinion mining for subjective measurement enabled a hermeneutic analysis process. The main objective of this research is to investigate the main factors that affect the performance of employees in virtual organization especially IT Companies and to show how these factors can be used for performance evaluation in virtual organization. Based on the review of literature, this study provides a unified domain Driven Data Mining (d3m) approach for evaluating data intelligence, domain intelligence, human intelligence, network intelligence, social intelligence, and meta synthesis of ubiquitous intelligence for performance appraisal in virtual organizations like IT Industries. This study examined opinion mining of virtual team members as subjective measure for their performance evaluation system. A phenomenological approach using support vector machine was used to Meta synthesize as ubiquitous intelligence. This D3M approach gives a valuable insight into the performance of employees in virtual organization and can give a useful help to practitioners to evaluate the performance of employees in virtual organizations.
{"title":"An ubiquitous domain Driven Data Mining approach for performance monitoring in virtual organizations using 360 Degree data mining & opinion mining","authors":"V. Suriyakumari, A. V. Kathiravan","doi":"10.1109/ICPRIME.2013.6496491","DOIUrl":"https://doi.org/10.1109/ICPRIME.2013.6496491","url":null,"abstract":"Performance evaluation in virtual organizations is one of the most important issues that have been considered due to the transition from industrial age to knowledge era. Virtual organizations, as one of the challenges of third millennium, which came to existence for enhancing organization's performance through outsourcing, are not excluding. A virtual organization and its smaller variant, the virtual team, is an organizational network that is structured and managed to function as an identifiable and complete organization. Determining what meanings virtual team members attach to performance evaluation system in IT Companies is a vital precursor to understand the effectiveness of the management practice, rendering this study a preliminary investigation. The literature confirms that perceptions of management practices in IT Industries can influence employee loyalty and role-related behaviors. Perceptions of unfairness can be more detrimental for geographically distributed workers in MNCs than for collocated teams. Although businesses continue to drive demands for virtual organizations, most contemporary studies of performance evaluation system are limited to traditional organizational settings. An interpretive, phenomenological domain Driven Data Mining (D3M) approach utilizing 360 Degree data mining for objective measurement and opinion mining for subjective measurement enabled a hermeneutic analysis process. The main objective of this research is to investigate the main factors that affect the performance of employees in virtual organization especially IT Companies and to show how these factors can be used for performance evaluation in virtual organization. Based on the review of literature, this study provides a unified domain Driven Data Mining (d3m) approach for evaluating data intelligence, domain intelligence, human intelligence, network intelligence, social intelligence, and meta synthesis of ubiquitous intelligence for performance appraisal in virtual organizations like IT Industries. This study examined opinion mining of virtual team members as subjective measure for their performance evaluation system. A phenomenological approach using support vector machine was used to Meta synthesize as ubiquitous intelligence. This D3M approach gives a valuable insight into the performance of employees in virtual organization and can give a useful help to practitioners to evaluate the performance of employees in virtual organizations.","PeriodicalId":123210,"journal":{"name":"2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115659268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：