Pub Date : 2014-12-01DOI: 10.1109/ICCIC.2014.7238422
M. Anusha, J. Sathiaseelan
K-means algorithm is sensitive to the initial cluster centers and clustering results diverge with different initial input which in turn falls into local optimum. Genetic Algorithms are randomized searching technique which provides a better optimal solution for fitness function of an optimization problem. This paper proposes an enhanced K-means Genetic Algorithm for optimal clustering of data (EKMG). The aim is to maximize the compactness the clusters with large separation between at least two clusters. The superiority of EKMG is compared with grouping genetic algorithm (GGA) by using real-life dataset. The experiment shows that EKMG reaches better optimal solution with high accuracy.
{"title":"An enhanced K-means genetic algorithms for optimal clustering","authors":"M. Anusha, J. Sathiaseelan","doi":"10.1109/ICCIC.2014.7238422","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238422","url":null,"abstract":"K-means algorithm is sensitive to the initial cluster centers and clustering results diverge with different initial input which in turn falls into local optimum. Genetic Algorithms are randomized searching technique which provides a better optimal solution for fitness function of an optimization problem. This paper proposes an enhanced K-means Genetic Algorithm for optimal clustering of data (EKMG). The aim is to maximize the compactness the clusters with large separation between at least two clusters. The superiority of EKMG is compared with grouping genetic algorithm (GGA) by using real-life dataset. The experiment shows that EKMG reaches better optimal solution with high accuracy.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128567639","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238320
A. S
Routine activity theory and social learning theory are two most important aspects of criminal psychology. Routine activity theory states that criminal acts are caused due to the presence of criminals, victims and the absence of guardians in time and place. As the number of collision of these elements in place and time increases, criminal acts will also increase even if the number of criminals or civilians remains the same within the vicinity of a city. Street robbery is a typical example of routine activity theory and the occurrence of which can be predicted using routine activity theory. Agent-based models allow simulation of diversity among individuals. Therefore agent based simulation of street robbery can be used to visualize how chronological occurrences of human activity influence the incidence of street robbery. The conceptual model identifies three classes of people-criminals, civilians and police with certain activity areas for each. Police exist only as agents of formal guardianship. Criminals with a tendency for crime will be in the search for their victims. Civilians without criminal tendency can be either victims or guardians. These agents are subjected to random walk through a street environment guided by a Q-learning module and the possible outcomes are analyzed. Through the time period some civilian agents with a tendency to commit crime and have criminal background or criminal companions may turn into criminals. This phenomenon reflects social learning theory. The results compare gang and single robberies, robberies made on kids and women, tests on criminal social learning and comparison of different guardian patrolling strategies.
{"title":"Agent based simulation of routine activity with social learning behavior","authors":"A. S","doi":"10.1109/ICCIC.2014.7238320","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238320","url":null,"abstract":"Routine activity theory and social learning theory are two most important aspects of criminal psychology. Routine activity theory states that criminal acts are caused due to the presence of criminals, victims and the absence of guardians in time and place. As the number of collision of these elements in place and time increases, criminal acts will also increase even if the number of criminals or civilians remains the same within the vicinity of a city. Street robbery is a typical example of routine activity theory and the occurrence of which can be predicted using routine activity theory. Agent-based models allow simulation of diversity among individuals. Therefore agent based simulation of street robbery can be used to visualize how chronological occurrences of human activity influence the incidence of street robbery. The conceptual model identifies three classes of people-criminals, civilians and police with certain activity areas for each. Police exist only as agents of formal guardianship. Criminals with a tendency for crime will be in the search for their victims. Civilians without criminal tendency can be either victims or guardians. These agents are subjected to random walk through a street environment guided by a Q-learning module and the possible outcomes are analyzed. Through the time period some civilian agents with a tendency to commit crime and have criminal background or criminal companions may turn into criminals. This phenomenon reflects social learning theory. The results compare gang and single robberies, robberies made on kids and women, tests on criminal social learning and comparison of different guardian patrolling strategies.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128269175","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238329
Jayanthi Kumari, H. S. Jayanna
This work address text-independent speaker verification with the constraint of limited data (<;15 seconds). The existing techniques for speaker verification work well for sufficient data (>1 minute). Developing techniques for verifying the speakers for limited data condition is a challenging issue since data available of speakers is very small nowadays. This is because people reluctant to give more data. In this work to extract features of speech signal Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) are used. The extracted features are modeled using Gaussian Mixture Model (GMM) and GMM-Universal Background Model (UBM) modeling techniques. The NIST-2003 database is used to carry-out the experiments. The experiments are evaluated for limited amount of training and testing speech data. The experimental observation indicates that the Equal Error Rate of LPCC features is less as compared to MFCC for limited data.
{"title":"Comparison of LPCC and MFCC features and GMM and GMM-UBM modeling for limited data speaker verification","authors":"Jayanthi Kumari, H. S. Jayanna","doi":"10.1109/ICCIC.2014.7238329","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238329","url":null,"abstract":"This work address text-independent speaker verification with the constraint of limited data (<;15 seconds). The existing techniques for speaker verification work well for sufficient data (>1 minute). Developing techniques for verifying the speakers for limited data condition is a challenging issue since data available of speakers is very small nowadays. This is because people reluctant to give more data. In this work to extract features of speech signal Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) are used. The extracted features are modeled using Gaussian Mixture Model (GMM) and GMM-Universal Background Model (UBM) modeling techniques. The NIST-2003 database is used to carry-out the experiments. The experiments are evaluated for limited amount of training and testing speech data. The experimental observation indicates that the Equal Error Rate of LPCC features is less as compared to MFCC for limited data.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128958028","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238395
Pallavi Chhikara, G. Matharu, V. Deep
Software defined networks (SDN) are an emerging technology that is being increasingly adopted by various network operators. These technologies provide new services and powerful analytics that help to transform the network and unfasten its intelligence to serve today's business demands. This paper briefs about the need for change in the current networking technology and explores the role of Open Flow protocol that is used by researchers to experiment with more realistic settings to provide for a new network architecture. Further, this paper discusses the advantages offered by SDN and the huge potential of OpenFlow based SDN. As SDN can simplify management of virtualized networks, enable cloud computing and reduce costs, the vendors would be encouraged to adopt SDN and OpenFlow. The objective of this paper is to provide an insight into the latest technology to the vendors to assist them in future enhancement of their switch products in the network.
{"title":"Towards OpenFlow based software defined networks","authors":"Pallavi Chhikara, G. Matharu, V. Deep","doi":"10.1109/ICCIC.2014.7238395","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238395","url":null,"abstract":"Software defined networks (SDN) are an emerging technology that is being increasingly adopted by various network operators. These technologies provide new services and powerful analytics that help to transform the network and unfasten its intelligence to serve today's business demands. This paper briefs about the need for change in the current networking technology and explores the role of Open Flow protocol that is used by researchers to experiment with more realistic settings to provide for a new network architecture. Further, this paper discusses the advantages offered by SDN and the huge potential of OpenFlow based SDN. As SDN can simplify management of virtualized networks, enable cloud computing and reduce costs, the vendors would be encouraged to adopt SDN and OpenFlow. The objective of this paper is to provide an insight into the latest technology to the vendors to assist them in future enhancement of their switch products in the network.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129006074","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238353
A. Kulkarni, A. Kumar
This paper presents an adaptive control strategy which combines the hierarchical control scheme with adaptive wavelet neural network for a class of multi-input multi-output (MIMO) underactuated systems with uncertain dynamics. Proposed scheme develops a systematic framework of the control components by applying hierarchical scheme to underactuated system. Wavelet neural networks are used to mimic the system uncertainties. Adaptive parameters of the wavelet network are tuned on line using gradient based approach. Uniformly ultimately bounded (UUB) stability of the closed loop system is analyzed in the sense of Lyapunov theory. Simulation results demonstrate the performance of proposed control scheme.
{"title":"Adaptive hierarchical control for a class of MIMO uncertain underactuated systems","authors":"A. Kulkarni, A. Kumar","doi":"10.1109/ICCIC.2014.7238353","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238353","url":null,"abstract":"This paper presents an adaptive control strategy which combines the hierarchical control scheme with adaptive wavelet neural network for a class of multi-input multi-output (MIMO) underactuated systems with uncertain dynamics. Proposed scheme develops a systematic framework of the control components by applying hierarchical scheme to underactuated system. Wavelet neural networks are used to mimic the system uncertainties. Adaptive parameters of the wavelet network are tuned on line using gradient based approach. Uniformly ultimately bounded (UUB) stability of the closed loop system is analyzed in the sense of Lyapunov theory. Simulation results demonstrate the performance of proposed control scheme.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125686645","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238471
Solaimurugan Vellaipandiyan, V. Srikrishnan
Map Reduce pioneered by Google is mainly employed in Big Data analytics. In Map Reduce environment, most of the algorithms are re-used for mining the data. Prediction of execution time and system overhead of MapReduce job is very vital, from which performance shall be ascertained. Cloud computing is widely used as a computing platform in business and academic communities. Performance plays a major role, when user runs an application in the cloud. User may want to estimate the application execution time (latency) before submitting a Task or a Job. Hadoop clusters are deployed on Cloud environment performing the experiment. System overhead is determined by running Map Reduce job over Hadoop Clusters. While performing the experiment, metrics such as network I/O, CPU, Swap utilization, Time to complete the job and RSS, VSZ were captured and evaluated in order to diagnose, how performance of Hadoop is influenced by reconstructing the block size and split size with respect to block size.
{"title":"An approach to discover the best-fit factors for the optimal performance of Hadoop map reduce in virtualized environment","authors":"Solaimurugan Vellaipandiyan, V. Srikrishnan","doi":"10.1109/ICCIC.2014.7238471","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238471","url":null,"abstract":"Map Reduce pioneered by Google is mainly employed in Big Data analytics. In Map Reduce environment, most of the algorithms are re-used for mining the data. Prediction of execution time and system overhead of MapReduce job is very vital, from which performance shall be ascertained. Cloud computing is widely used as a computing platform in business and academic communities. Performance plays a major role, when user runs an application in the cloud. User may want to estimate the application execution time (latency) before submitting a Task or a Job. Hadoop clusters are deployed on Cloud environment performing the experiment. System overhead is determined by running Map Reduce job over Hadoop Clusters. While performing the experiment, metrics such as network I/O, CPU, Swap utilization, Time to complete the job and RSS, VSZ were captured and evaluated in order to diagnose, how performance of Hadoop is influenced by reconstructing the block size and split size with respect to block size.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"81 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133007581","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238286
K. Sagar, P. Palanisamy
This paper presents a pilot based channel estimation method for MIMO-OFDM systems, the combination of MIMO with OFDM is used to increase the bit rate as well as capacity. The existing channel estimation methods uses more OFDM periods (STBC) and gives high MSE(conventional pilots). The proposed channel estimation method has very low complexity, With these orthogonal pilots we can obtain the optimal MSE which is 25% of conventional pilot method MSE and it effectively utilizes OFDM periods compared to STBC pilot method. Experimental results show that the proposed method achieves higher accuracy and gives better performance.
{"title":"Optimal orthogonal pilots design for MIMO-OFDM channel estimation","authors":"K. Sagar, P. Palanisamy","doi":"10.1109/ICCIC.2014.7238286","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238286","url":null,"abstract":"This paper presents a pilot based channel estimation method for MIMO-OFDM systems, the combination of MIMO with OFDM is used to increase the bit rate as well as capacity. The existing channel estimation methods uses more OFDM periods (STBC) and gives high MSE(conventional pilots). The proposed channel estimation method has very low complexity, With these orthogonal pilots we can obtain the optimal MSE which is 25% of conventional pilot method MSE and it effectively utilizes OFDM periods compared to STBC pilot method. Experimental results show that the proposed method achieves higher accuracy and gives better performance.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132100591","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238289
Nidhi Bansal, M. Dutta
Minimizing the total allocation cost is an unavoidable issue needing to be deal with in task scheduling of cloud computing for processing all the tasks. In this paper, the comparative study has been done among the priority algorithms and secondly between the priority and non-priority task scheduling algorithms. In order to emulate the precedence relation of task, the task scheduling algorithms enumerate the priority of tasks according to the specific attributes of task, and then sort tasks by priority. The total allocation costs for each scheduling algorithm are calculated. The experimental results based on cloudsim3.0 toolkit with NetBeans IDE8.0 shows that the ABC algorithm (priority based) achieves good performance in cost parameter with the comparison of QoS driven, virtual machine tree (both are priority based) and traditional scheduling algorithms but the comparison of ABC (priority based) and PSO (non priority based) shows that the non priority algorithm gives best performance due to less waiting time to allocation task.
{"title":"Performance evaluation of task scheduling with priority and non-priority in cloud computing","authors":"Nidhi Bansal, M. Dutta","doi":"10.1109/ICCIC.2014.7238289","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238289","url":null,"abstract":"Minimizing the total allocation cost is an unavoidable issue needing to be deal with in task scheduling of cloud computing for processing all the tasks. In this paper, the comparative study has been done among the priority algorithms and secondly between the priority and non-priority task scheduling algorithms. In order to emulate the precedence relation of task, the task scheduling algorithms enumerate the priority of tasks according to the specific attributes of task, and then sort tasks by priority. The total allocation costs for each scheduling algorithm are calculated. The experimental results based on cloudsim3.0 toolkit with NetBeans IDE8.0 shows that the ABC algorithm (priority based) achieves good performance in cost parameter with the comparison of QoS driven, virtual machine tree (both are priority based) and traditional scheduling algorithms but the comparison of ABC (priority based) and PSO (non priority based) shows that the non priority algorithm gives best performance due to less waiting time to allocation task.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128833034","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238501
Atallah Al-Shatnawi
Text-line skew detection and correction is the first step in Arabic document recognition and analysis. It is a crucial pre-processing stage of Arabic Character Recognition (ACR). It has a direct effect on the dependability and efficiency of other system stages such as baseline detection, segmentation and feature extraction stages. In this paper an efficient skew detection and correction method for Arabic handwritten text-line based on sub-words bounding is presented. It is constructed from three stages including: pre-processing, skew detection and skew correction stages. The proposed method estimates a text-line baseline based on calculating the middle point for its sub-words bounding. Then align the text-line components on the estimated baseline. The proposed method is implemented on 3960 text-line handwritten images, which were written by 40 writers. It is discussed with the horizontal projection method in terms of effectiveness. The proposed method obtained an accuracy ratio of 96.15%, and takes 6.7 seconds as average time. It can also automatically detect text baselines of documents with any orientation.
{"title":"A skew detection and correction technique for Arabic script text-line based on subwords bounding","authors":"Atallah Al-Shatnawi","doi":"10.1109/ICCIC.2014.7238501","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238501","url":null,"abstract":"Text-line skew detection and correction is the first step in Arabic document recognition and analysis. It is a crucial pre-processing stage of Arabic Character Recognition (ACR). It has a direct effect on the dependability and efficiency of other system stages such as baseline detection, segmentation and feature extraction stages. In this paper an efficient skew detection and correction method for Arabic handwritten text-line based on sub-words bounding is presented. It is constructed from three stages including: pre-processing, skew detection and skew correction stages. The proposed method estimates a text-line baseline based on calculating the middle point for its sub-words bounding. Then align the text-line components on the estimated baseline. The proposed method is implemented on 3960 text-line handwritten images, which were written by 40 writers. It is discussed with the horizontal projection method in terms of effectiveness. The proposed method obtained an accuracy ratio of 96.15%, and takes 6.7 seconds as average time. It can also automatically detect text baselines of documents with any orientation.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125716888","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 : 2014-12-01DOI: 10.1109/ICCIC.2014.7238414
K. Sumangali, R. Aishwarya, E. Hemavathi, A. Niraimathi
Association rule mining is a standard technique used for finding the relationships among the itemsets in a database. The method of extracting the frequent itemsets from the database using existing algorithms has several disadvantages such as generation of large number of candidate itemsets, increase in computational time and database scan. With this aim, the paper proposes Mining Interesting Itemsets (MIIS) algorithm which combines the features of partition algorithm and FP tree which reduces the database scan and produces the reduced itemsets from the transactions. The reduced itemsets are validated using the mathematical measures.
{"title":"Mining interesting itemsets from transactional database","authors":"K. Sumangali, R. Aishwarya, E. Hemavathi, A. Niraimathi","doi":"10.1109/ICCIC.2014.7238414","DOIUrl":"https://doi.org/10.1109/ICCIC.2014.7238414","url":null,"abstract":"Association rule mining is a standard technique used for finding the relationships among the itemsets in a database. The method of extracting the frequent itemsets from the database using existing algorithms has several disadvantages such as generation of large number of candidate itemsets, increase in computational time and database scan. With this aim, the paper proposes Mining Interesting Itemsets (MIIS) algorithm which combines the features of partition algorithm and FP tree which reduces the database scan and produces the reduced itemsets from the transactions. The reduced itemsets are validated using the mathematical measures.","PeriodicalId":187874,"journal":{"name":"2014 IEEE International Conference on Computational Intelligence and Computing Research","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125721394","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：