Pub Date : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954240
Yashwant Singh Patel, N. Singh, Lalit Kumar Vashishtha
Computational Complexity is a fundamental research area in the field of computer science. It has attracted lots of interest of various researchers. In past, vast number of sorting algorithms has been proposed by various researchers. To efficiently optimize any sorting problem having large number of elements requires O(nlogn) time in average case by existing sorting techniques. This paper presents a new sorting technique based on divide & conquer approach, named as Fuse sort algorithm, an approach of comparison based sorting with O(nloglogn) time and linear space. The priory and mathematical analysis of proposed sorting algorithm is given and a case study with merge sort is performed based on several factors.
{"title":"Fuse sort algorithm a proposal of divide & conquer based sorting approach with O(nloglogn) time and linear space complexity","authors":"Yashwant Singh Patel, N. Singh, Lalit Kumar Vashishtha","doi":"10.1109/ICDMIC.2014.6954240","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954240","url":null,"abstract":"Computational Complexity is a fundamental research area in the field of computer science. It has attracted lots of interest of various researchers. In past, vast number of sorting algorithms has been proposed by various researchers. To efficiently optimize any sorting problem having large number of elements requires O(nlogn) time in average case by existing sorting techniques. This paper presents a new sorting technique based on divide & conquer approach, named as Fuse sort algorithm, an approach of comparison based sorting with O(nloglogn) time and linear space. The priory and mathematical analysis of proposed sorting algorithm is given and a case study with merge sort is performed based on several factors.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125916434","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954257
S Aditya Gautam, N. Verma
The planning of path for Unmanned Aerial Vehicle (UAV) is always considered to be a vital task. Path planning for UAV for avoiding the obstacle in its path can be accomplished by finding the solution for an optimization problem. Genetic Algorithm which is a global optimization tool can be of great use to solve the optimization problem for path planning of UAV. Artificial Neural Network (ANN) works well for function fitting quickly and can be used to approximate almost any function. The Genetic Algorithms are good at converging to the globally optimum solution generation by generation. Each generation is expected to be better than its previous generation. Neural Networks work faster than Genetic Algorithms for finding the solution to a given problem but may get converged to local optimum instead of global optimum. In this paper a new method for path planning for UAV to avoid obstacle coming in its path based on the combination of Genetic Algorithms and Artificial Neural Networks has been proposed in which the output generated from the Genetic Algorithms is used to train the network of Artificial Neural Networks. The model for path planning is based on 3D digital map.
{"title":"Path planning for unmanned aerial vehicle based on genetic algorithm & artificial neural network in 3D","authors":"S Aditya Gautam, N. Verma","doi":"10.1109/ICDMIC.2014.6954257","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954257","url":null,"abstract":"The planning of path for Unmanned Aerial Vehicle (UAV) is always considered to be a vital task. Path planning for UAV for avoiding the obstacle in its path can be accomplished by finding the solution for an optimization problem. Genetic Algorithm which is a global optimization tool can be of great use to solve the optimization problem for path planning of UAV. Artificial Neural Network (ANN) works well for function fitting quickly and can be used to approximate almost any function. The Genetic Algorithms are good at converging to the globally optimum solution generation by generation. Each generation is expected to be better than its previous generation. Neural Networks work faster than Genetic Algorithms for finding the solution to a given problem but may get converged to local optimum instead of global optimum. In this paper a new method for path planning for UAV to avoid obstacle coming in its path based on the combination of Genetic Algorithms and Artificial Neural Networks has been proposed in which the output generated from the Genetic Algorithms is used to train the network of Artificial Neural Networks. The model for path planning is based on 3D digital map.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116416375","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954226
Tribikram Pradhan, S. R. Mishra, V. K. Jain
Now-a-days the storage of a huge amount of data is very easy due to use of modern technologies, but the useful information that remains inside that storage media is unknown to us. The data mining provides us different techniques and rules that can be used to analyze and extract unknown rules, hidden patterns and associations from the previously stored data. Data mining technology is well implemented in the field of marketing, finance but not so familiar in education field. In this paper we are applying Apriori algorithm to find the hidden interest of a student, while selecting a subject from a group of elective subjects, the system will suggest some elective subjects according to student's interest, and the research paper related to that subjects or domain. The implemented Association rules and algorithms guide the students in an effective way to achieve excellence in their research based learning.
{"title":"An effective way to achieve excellence in research based learning using association rules","authors":"Tribikram Pradhan, S. R. Mishra, V. K. Jain","doi":"10.1109/ICDMIC.2014.6954226","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954226","url":null,"abstract":"Now-a-days the storage of a huge amount of data is very easy due to use of modern technologies, but the useful information that remains inside that storage media is unknown to us. The data mining provides us different techniques and rules that can be used to analyze and extract unknown rules, hidden patterns and associations from the previously stored data. Data mining technology is well implemented in the field of marketing, finance but not so familiar in education field. In this paper we are applying Apriori algorithm to find the hidden interest of a student, while selecting a subject from a group of elective subjects, the system will suggest some elective subjects according to student's interest, and the research paper related to that subjects or domain. The implemented Association rules and algorithms guide the students in an effective way to achieve excellence in their research based learning.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124389609","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954247
K. Gupta, Rashmi Gupta
Iris recognition is one of the most powerful techniques for biometric identification. The requirement for smart environments is to acquire multiple iris codes from the same eye and evaluate which bits are the most consistent bits in the iris code. When the acquired images are noisy, the inconsistent bits in the iris code should be masked to improve performance. This paper thoroughly investigates the use of multiple training samples for enrollment. Based on this, an enhanced iris recognition approach is proposed for the smart environments employing the fusion of a set of iris images of a given eye using the most consistent feature data. The algorithm reduces the database size and accelerates the matching process. The Chinese Academy of Sciences - Institute of Automation (CASIA) database is used to simulate the studies. The comparison of probe to multiple gallery samples in the proposed approach has been shown to improve the performance of the system compared to the existing Daugman algorithm.
{"title":"Iris recognition system for smart environments","authors":"K. Gupta, Rashmi Gupta","doi":"10.1109/ICDMIC.2014.6954247","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954247","url":null,"abstract":"Iris recognition is one of the most powerful techniques for biometric identification. The requirement for smart environments is to acquire multiple iris codes from the same eye and evaluate which bits are the most consistent bits in the iris code. When the acquired images are noisy, the inconsistent bits in the iris code should be masked to improve performance. This paper thoroughly investigates the use of multiple training samples for enrollment. Based on this, an enhanced iris recognition approach is proposed for the smart environments employing the fusion of a set of iris images of a given eye using the most consistent feature data. The algorithm reduces the database size and accelerates the matching process. The Chinese Academy of Sciences - Institute of Automation (CASIA) database is used to simulate the studies. The comparison of probe to multiple gallery samples in the proposed approach has been shown to improve the performance of the system compared to the existing Daugman algorithm.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124124865","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954231
Anubha Jain, Jaya Sharma
A great deal of computer vision research and study is dedicated to the systems designed to detect and analyze computer printed documents and human written text. Optical Character Recognition (OCR) refers to the process of converting images of hand-written, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size. In this paper we have combined the functionality of Optical Character Recognition and have focused on its applications like Image Sudoku Solver, Car License Plate Detection and Recognition, Handwritten and Computer Printed Documents Recognition. This paper develops a user friendly application for performing image to text conversion. The developed system is organized as a set of modules, each dedicated to a specific application. Car License Plate Detection and Recognition system extracts out the License plate accurately and produce an effective recognition of the characters in the License Plate. With the proposed methodology, we have been able to achieve results with 96% accuracy for the tested images. Image Sudoku Solver is intended to work with Sudoku Puzzle images by extracting out the numbers, boundaries from them and then solving the puzzle. In this we are able to extract, recognize and solve around 98% of Sudoku Puzzles being tested for the purpose. Online Handwriting Recognition accomplishes the real time recognition of user's Handwriting from a Mouse or a Laptop Touchpad.
{"title":"Classification and interpretation of characters in multi-application OCR system","authors":"Anubha Jain, Jaya Sharma","doi":"10.1109/ICDMIC.2014.6954231","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954231","url":null,"abstract":"A great deal of computer vision research and study is dedicated to the systems designed to detect and analyze computer printed documents and human written text. Optical Character Recognition (OCR) refers to the process of converting images of hand-written, typewritten, or printed text into a format understood by machines for the purpose of editing, indexing/searching, and a reduction in storage size. In this paper we have combined the functionality of Optical Character Recognition and have focused on its applications like Image Sudoku Solver, Car License Plate Detection and Recognition, Handwritten and Computer Printed Documents Recognition. This paper develops a user friendly application for performing image to text conversion. The developed system is organized as a set of modules, each dedicated to a specific application. Car License Plate Detection and Recognition system extracts out the License plate accurately and produce an effective recognition of the characters in the License Plate. With the proposed methodology, we have been able to achieve results with 96% accuracy for the tested images. Image Sudoku Solver is intended to work with Sudoku Puzzle images by extracting out the numbers, boundaries from them and then solving the puzzle. In this we are able to extract, recognize and solve around 98% of Sudoku Puzzles being tested for the purpose. Online Handwriting Recognition accomplishes the real time recognition of user's Handwriting from a Mouse or a Laptop Touchpad.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"17 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130981452","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954250
Chandni Naik, A. Kharwar, Mukesh Patel
Sequential pattern mining is helpful methodology to discover customer purchasing behaviour from large sequence database. Sequential pattern mining can be used in medical records, marketing, sales analysis, and web log analysis and so on. The traditional sequential pattern mining does not give the pattern which is recent and profitable. So, RFM-based sequential pattern mining techniques is introduced. Although RFM-based sequential pattern mining gives buying patterns which are recently active and profitable however it does not give the time interval between each and every items. To discover a time interval, RFM-TI algorithm is proposed. The advantages of considering multi time interval is, from that we are able to realize what customer would possibly buy in next “h” step rather than next step. The experimental evaluation shows that the proposed method can discover more valuable patterns than RFM-based sequential pattern mining.
{"title":"Knowledge discovery of weighted RFM sequential patterns with multi time interval from customer sequence database","authors":"Chandni Naik, A. Kharwar, Mukesh Patel","doi":"10.1109/ICDMIC.2014.6954250","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954250","url":null,"abstract":"Sequential pattern mining is helpful methodology to discover customer purchasing behaviour from large sequence database. Sequential pattern mining can be used in medical records, marketing, sales analysis, and web log analysis and so on. The traditional sequential pattern mining does not give the pattern which is recent and profitable. So, RFM-based sequential pattern mining techniques is introduced. Although RFM-based sequential pattern mining gives buying patterns which are recently active and profitable however it does not give the time interval between each and every items. To discover a time interval, RFM-TI algorithm is proposed. The advantages of considering multi time interval is, from that we are able to realize what customer would possibly buy in next “h” step rather than next step. The experimental evaluation shows that the proposed method can discover more valuable patterns than RFM-based sequential pattern mining.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130203335","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954253
Arti Jain, Divakar Yadav, Dev Tayal
In this paper, we propose a state-of-art association rule mining algorithm for Hindi NER. Association rules are one of the key components of the data mining. Mined rules are of - TYPE 1, TYPE 2 and Type 3 i.e. dictionary, bi-gram and feature rules respectively. We consider corpus of news articles (100 training and 50 test sets) from leading Hindi newspapers. Hindi NER shows significant increase in performance when TYPE 2 rules are combined with TYPE 1 or with TYPE 3.
在本文中，我们提出了一种最先进的印地语NER关联规则挖掘算法。关联规则是数据挖掘的关键组成部分之一。挖掘的规则类型为- TYPE 1、TYPE 2和TYPE 3，即字典规则、双图规则和特征规则。我们考虑来自主要印地语报纸的新闻文章语料库(100个训练集和50个测试集)。当类型2规则与类型1或类型3相结合时，印地语NER表现出显著的性能提高。
{"title":"NER for Hindi language using association rules","authors":"Arti Jain, Divakar Yadav, Dev Tayal","doi":"10.1109/ICDMIC.2014.6954253","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954253","url":null,"abstract":"In this paper, we propose a state-of-art association rule mining algorithm for Hindi NER. Association rules are one of the key components of the data mining. Mined rules are of - TYPE 1, TYPE 2 and Type 3 i.e. dictionary, bi-gram and feature rules respectively. We consider corpus of news articles (100 training and 50 test sets) from leading Hindi newspapers. Hindi NER shows significant increase in performance when TYPE 2 rules are combined with TYPE 1 or with TYPE 3.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121816606","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954242
Prem Shankar Singh Aydav, S. Minz
Fuzzy c-means clustering technique has been popularly used for remote sensing image data classification. However as per the studies the classical fuzzy c-means clustering algorithm has been able to achieve less accuracy due to spatial relationship existence and multi class existence in remotely sensed images. Remote sensing images contain large number of classes but the probability of a pixel belonging to some classes may be low. Traditional fuzzy c-means algorithm considers all classes simultaneously during clustering process. In this paper generalized fuzzy c-means has been applied in exploring k nearest neighbors approach out of c cluster centers. Spatial information has been also integrated with generalized fuzzy c-means technique. The experimental results show that the generalized fuzzy c-means technique with spatial information yields better results than traditional fuzzy c-means technique.
{"title":"Generalized fuzzy c-means with spatial information for clustering of remote sensing images","authors":"Prem Shankar Singh Aydav, S. Minz","doi":"10.1109/ICDMIC.2014.6954242","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954242","url":null,"abstract":"Fuzzy c-means clustering technique has been popularly used for remote sensing image data classification. However as per the studies the classical fuzzy c-means clustering algorithm has been able to achieve less accuracy due to spatial relationship existence and multi class existence in remotely sensed images. Remote sensing images contain large number of classes but the probability of a pixel belonging to some classes may be low. Traditional fuzzy c-means algorithm considers all classes simultaneously during clustering process. In this paper generalized fuzzy c-means has been applied in exploring k nearest neighbors approach out of c cluster centers. Spatial information has been also integrated with generalized fuzzy c-means technique. The experimental results show that the generalized fuzzy c-means technique with spatial information yields better results than traditional fuzzy c-means technique.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131420498","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954254
N. Singh, Yashwant Singh Patel, Utpalendu Das, Ananya Chatterjee
Cloud Computing is a vast infrastructural and rising pool, which provides huge storage of data in one sphere. Organizations, now a days are in the marathon of equipping the whole system in a cloud form. The attackers evaluating data for a long time to extract the valued information to perform data mining based attacks on the cloud. In the recent architectures the data is sited in a single or distributed cloud provider. It gives the opportunity to the cloud providers and attackers to unauthorized access from cloud and also gives the chance to analyze the client data for a long time to extract the sensitive information, which is responsible for the privacy violation of clients. This paper proposes an approach that firstly maintains the confidentiality, integrity, and authentication for the stored data in cloud. Secondly, it presents distributed storage cloud architecture, which includes the description of trusted computing work group (TCG) and trusted platform module (TPM). It provides hardware authentication for trustworthy computing platform and also uses Kerberos authentication to avoid software attacks. This proposed approach establishes file locality by clustering the related data based on their physical distance and effective matching with client applications. It supports efficient clustering and reduces communication cost in large-scale cloud computing applications.
{"title":"NUYA: An encrypted mechanism for securing cloud data from data mining attacks","authors":"N. Singh, Yashwant Singh Patel, Utpalendu Das, Ananya Chatterjee","doi":"10.1109/ICDMIC.2014.6954254","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954254","url":null,"abstract":"Cloud Computing is a vast infrastructural and rising pool, which provides huge storage of data in one sphere. Organizations, now a days are in the marathon of equipping the whole system in a cloud form. The attackers evaluating data for a long time to extract the valued information to perform data mining based attacks on the cloud. In the recent architectures the data is sited in a single or distributed cloud provider. It gives the opportunity to the cloud providers and attackers to unauthorized access from cloud and also gives the chance to analyze the client data for a long time to extract the sensitive information, which is responsible for the privacy violation of clients. This paper proposes an approach that firstly maintains the confidentiality, integrity, and authentication for the stored data in cloud. Secondly, it presents distributed storage cloud architecture, which includes the description of trusted computing work group (TCG) and trusted platform module (TPM). It provides hardware authentication for trustworthy computing platform and also uses Kerberos authentication to avoid software attacks. This proposed approach establishes file locality by clustering the related data based on their physical distance and effective matching with client applications. It supports efficient clustering and reduces communication cost in large-scale cloud computing applications.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116083428","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 : 2014-11-13DOI: 10.1109/ICDMIC.2014.6954265
S. Gomathi, V. Narayani
Discovering hidden patterns in medical data and relationship between them is often fallow. Classification technique in data mining is used to discover the hidden knowledge from enormous data. This work is done on predicting the risk of Systemic Lupus Erythematosus (SLE)/ Lupus using data mining classification technique. Decision tree algorithm is used for training set of data. A new proposed framework and an enhanced algorithm is proposed. The classification algorithm is used to reduce the complexity and to increase the performance.
{"title":"Systemic Lupus Erythematosus manifestation using ID3 algorithm - A clinical analysis","authors":"S. Gomathi, V. Narayani","doi":"10.1109/ICDMIC.2014.6954265","DOIUrl":"https://doi.org/10.1109/ICDMIC.2014.6954265","url":null,"abstract":"Discovering hidden patterns in medical data and relationship between them is often fallow. Classification technique in data mining is used to discover the hidden knowledge from enormous data. This work is done on predicting the risk of Systemic Lupus Erythematosus (SLE)/ Lupus using data mining classification technique. Decision tree algorithm is used for training set of data. A new proposed framework and an enhanced algorithm is proposed. The classification algorithm is used to reduce the complexity and to increase the performance.","PeriodicalId":138199,"journal":{"name":"2014 International Conference on Data Mining and Intelligent Computing (ICDMIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124071490","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}
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