Pub Date : 2011-08-04DOI: 10.1109/DMO.2011.5976517
Imam Mujahidin Iqbal, N. Aziz
An accurate and simple model is essential to implement a model based controller. Wiener model is one of the simplest nonlinear models that can represent any nonlinear process. However, in Wiener Model development, there are several identification approaches available and need to be selected to produce the most accurate model. In this work, the nonlinear - linear approach, the linear - nonlinear approach, and the simultaneous approach are compared in identification of the Wiener model for nonlinear pH neutralization process. The parameters of linear block and the inverse of nonlinear block were obtained from several sets of data that are generated. These approaches are then compared in terms of model accuracy, calculation time, data requirement, and their flexibility.
{"title":"Comparison of various Wiener model identification approach in modelling nonlinear process","authors":"Imam Mujahidin Iqbal, N. Aziz","doi":"10.1109/DMO.2011.5976517","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976517","url":null,"abstract":"An accurate and simple model is essential to implement a model based controller. Wiener model is one of the simplest nonlinear models that can represent any nonlinear process. However, in Wiener Model development, there are several identification approaches available and need to be selected to produce the most accurate model. In this work, the nonlinear - linear approach, the linear - nonlinear approach, and the simultaneous approach are compared in identification of the Wiener model for nonlinear pH neutralization process. The parameters of linear block and the inverse of nonlinear block were obtained from several sets of data that are generated. These approaches are then compared in terms of model accuracy, calculation time, data requirement, and their flexibility.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126551809","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976503
Zalizah Awang Long, A. Bakar, Abdul Razak Hamdan
Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. While Association Rules Mining (ARM) algorithm especially the Apriori algorithm has been an active research work in recent years. Diverse improvement varies in term of producing more frequent items and also generating further k-length. The idea is to produce better pattern and more interesting rules. In this paper, we propose new approach for ARM based on Multiple Attribute Value within the non-binary search spaces. The proposed algorithm improves the existing frequent pattern mining by generating the most frequent values (item) within the attribute and generate candidate based on the frequent attribute value. The main idea of our work is to discover more meaningful frequent items and maximum k-length items. The experimental results show that our proposed MAV frequent pattern mining enhance the impact in generating more frequents items and maximum length
{"title":"Frequent pattern using Multiple Attribute Value for itemset generation","authors":"Zalizah Awang Long, A. Bakar, Abdul Razak Hamdan","doi":"10.1109/DMO.2011.5976503","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976503","url":null,"abstract":"Data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. While Association Rules Mining (ARM) algorithm especially the Apriori algorithm has been an active research work in recent years. Diverse improvement varies in term of producing more frequent items and also generating further k-length. The idea is to produce better pattern and more interesting rules. In this paper, we propose new approach for ARM based on Multiple Attribute Value within the non-binary search spaces. The proposed algorithm improves the existing frequent pattern mining by generating the most frequent values (item) within the attribute and generate candidate based on the frequent attribute value. The main idea of our work is to discover more meaningful frequent items and maximum k-length items. The experimental results show that our proposed MAV frequent pattern mining enhance the impact in generating more frequents items and maximum length","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122540991","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976512
R. Yaakob, N. Mustapha, A. Nuruddin, I. S. Sitanggang
Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. Determining the weights for each criterion is based on expert knowledge or the previous experienced of the developers that may result too subjective models. In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. The algorithm is applied on historic forest fires data for a district in Riau namely Rokan Hilir to develop a model for forest fires risk. The modeling forest fire risk includes variables related to physical as well as social and economic. The result is a spatial decision tree containing 138 leaves with distance to nearest river as the first test attribute.
{"title":"Modeling forest fires risk using spatial decision tree","authors":"R. Yaakob, N. Mustapha, A. Nuruddin, I. S. Sitanggang","doi":"10.1109/DMO.2011.5976512","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976512","url":null,"abstract":"Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. Determining the weights for each criterion is based on expert knowledge or the previous experienced of the developers that may result too subjective models. In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. The algorithm is applied on historic forest fires data for a district in Riau namely Rokan Hilir to develop a model for forest fires risk. The modeling forest fire risk includes variables related to physical as well as social and economic. The result is a spatial decision tree containing 138 leaves with distance to nearest river as the first test attribute.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128739178","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976535
Zainal Apandi, N. Mustapha, L. S. Affendey
With the enormous amount of video data especially with the existence of the noisy and irrelevant information, it would be difficult for a typical detection process to capture a small portion of targeted due to the class imbalance problem. In this paper, class imbalance referred to a very small percentage of positive instance versus negative instances, where the negative instances dominate the detection model, resulting in the degradation of the detection performance. This paper proposed an Integrated Weight Linear (IWL) method that integrate weight linear algorithm (WL) with principle component analysis (PCA) to eliminate imbalanced dataset in soccer video data. PCA is adopted in the first phase with the aim to alleviates the imbalanced data and prepared the reduced instances to the next phase. In the second phase, the reduces instances are refined using the weight linear algorithm. The experiment results using 9 soccer video demonstrate that the integration of PCA and WL is capable to alleviates the imbalanced problem and able to improve classification performance in video data.
{"title":"Evaluating Integrated Weight Linear method to class imbalanced learning in video data","authors":"Zainal Apandi, N. Mustapha, L. S. Affendey","doi":"10.1109/DMO.2011.5976535","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976535","url":null,"abstract":"With the enormous amount of video data especially with the existence of the noisy and irrelevant information, it would be difficult for a typical detection process to capture a small portion of targeted due to the class imbalance problem. In this paper, class imbalance referred to a very small percentage of positive instance versus negative instances, where the negative instances dominate the detection model, resulting in the degradation of the detection performance. This paper proposed an Integrated Weight Linear (IWL) method that integrate weight linear algorithm (WL) with principle component analysis (PCA) to eliminate imbalanced dataset in soccer video data. PCA is adopted in the first phase with the aim to alleviates the imbalanced data and prepared the reduced instances to the next phase. In the second phase, the reduces instances are refined using the weight linear algorithm. The experiment results using 9 soccer video demonstrate that the integration of PCA and WL is capable to alleviates the imbalanced problem and able to improve classification performance in video data.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129871820","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976532
Mouna Jamom, M. Ayob, Mohammed Hadwan
Nurse Rostering Problem (NRP) concerns about producing a high quality workable duty roster for the available staff nurses. The aim of this work is to present a greedy constructive heuristic algorithm to generate a feasible initial solution by satisfying the hard constraints. Basically the initial solution includes three steps: first we start by designing a group of shift patterns based on hard and soft constraints. Then, those patterns are rotated for predefined positions and allocated to each nurse. Finally; if the solution is not feasible we use a repair mechanism. In this work, a real world problem from Universiti Kebangsaan Malaysia Medical Centre (UKMMC) is used to test the proposed algorithm. The resulting roster demonstrates that our proposed algorithm generates a good quality duty roster in a reasonable computational time for our case study.
{"title":"A greedy constructive approach for Nurse Rostering Problem","authors":"Mouna Jamom, M. Ayob, Mohammed Hadwan","doi":"10.1109/DMO.2011.5976532","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976532","url":null,"abstract":"Nurse Rostering Problem (NRP) concerns about producing a high quality workable duty roster for the available staff nurses. The aim of this work is to present a greedy constructive heuristic algorithm to generate a feasible initial solution by satisfying the hard constraints. Basically the initial solution includes three steps: first we start by designing a group of shift patterns based on hard and soft constraints. Then, those patterns are rotated for predefined positions and allocated to each nurse. Finally; if the solution is not feasible we use a repair mechanism. In this work, a real world problem from Universiti Kebangsaan Malaysia Medical Centre (UKMMC) is used to test the proposed algorithm. The resulting roster demonstrates that our proposed algorithm generates a good quality duty roster in a reasonable computational time for our case study.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123312508","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976525
Mohammed Hadwan, M. Ayob
The paper, at hand, introduces a semi-cyclic shift patterns approach (SCSPA) that solves nurse rostering problem (NRP) at the Medical Centre, Universiti Kebangsaan Malaysia (UKMMC). Since night shift is the most problematic shift to assign due to the extra constraints that it has, the paper proposes a combination of semi-cyclic approach, which first allocates a predesigned night shift patterns cyclically, then allocates a combined morning and evening shift patterns in a non-cyclic manner until fulfilling the hard constraints. This is different from our previous work that adopted a non-cyclic shift pattern approach (NCSPA) to construct all of the possible valid shift patterns, which were a combination of morning, evening and night shifts which were incorporated to yield one-week shift patterns. Next, two shift patterns of one-week were allocated for each nurse until construct the initial roster. This paper presents a comparison between the proposed semi-cyclic approach and the previous non-cyclic approach. Beside the minimum violation penalty, we count the number of good patterns that each algorithm produces in order to measure the quality of constructed duty roster. Then, the approach applies simulated annealing algorithm in order to improve the overall produced roster as to enhance the initial roster that resulted from both algorithms. By using a semi-cyclic approach, two benefits over our previous work are gained, (i) the number of constructed shift patterns decreased remarkably, thus reduces the construction time; and (ii) allocating night shift patterns fairly for all nurses becomes more manageable. Based on the obtained results, the semi-cyclic approach yields a better duty roster as it produces more good patterns compared to our previous Non-cyclic approach.
{"title":"A semi-cyclic shift patterns approach for nurse rostering problems","authors":"Mohammed Hadwan, M. Ayob","doi":"10.1109/DMO.2011.5976525","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976525","url":null,"abstract":"The paper, at hand, introduces a semi-cyclic shift patterns approach (SCSPA) that solves nurse rostering problem (NRP) at the Medical Centre, Universiti Kebangsaan Malaysia (UKMMC). Since night shift is the most problematic shift to assign due to the extra constraints that it has, the paper proposes a combination of semi-cyclic approach, which first allocates a predesigned night shift patterns cyclically, then allocates a combined morning and evening shift patterns in a non-cyclic manner until fulfilling the hard constraints. This is different from our previous work that adopted a non-cyclic shift pattern approach (NCSPA) to construct all of the possible valid shift patterns, which were a combination of morning, evening and night shifts which were incorporated to yield one-week shift patterns. Next, two shift patterns of one-week were allocated for each nurse until construct the initial roster. This paper presents a comparison between the proposed semi-cyclic approach and the previous non-cyclic approach. Beside the minimum violation penalty, we count the number of good patterns that each algorithm produces in order to measure the quality of constructed duty roster. Then, the approach applies simulated annealing algorithm in order to improve the overall produced roster as to enhance the initial roster that resulted from both algorithms. By using a semi-cyclic approach, two benefits over our previous work are gained, (i) the number of constructed shift patterns decreased remarkably, thus reduces the construction time; and (ii) allocating night shift patterns fairly for all nurses becomes more manageable. Based on the obtained results, the semi-cyclic approach yields a better duty roster as it produces more good patterns compared to our previous Non-cyclic approach.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115927871","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976534
M. Sainin, R. Alfred
Feature selection for data mining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of features) for the data in order to achieve similar or better classification rate. Currently, there are three types of feature selection methods: filter, wrapper and embedded. This paper describes a genetic based wrapper approach that optimizes feature selection process embedded in a classification technique called a supervised Nearest Neighbour Distance Matrix (NNDM). This method is implemented and tested on several datasets obtained from the UCI Machine Learning Repository and other datasets. The results demonstrate a significant impact on the predictive accuracy for feature selection combined with the supervised NNDM in classifying new instances. Therefore it can be used in other applications that require feature dimension reduction such as image and bioinformatics classifications.
{"title":"A genetic based wrapper feature selection approach using Nearest Neighbour Distance Matrix","authors":"M. Sainin, R. Alfred","doi":"10.1109/DMO.2011.5976534","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976534","url":null,"abstract":"Feature selection for data mining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of features) for the data in order to achieve similar or better classification rate. Currently, there are three types of feature selection methods: filter, wrapper and embedded. This paper describes a genetic based wrapper approach that optimizes feature selection process embedded in a classification technique called a supervised Nearest Neighbour Distance Matrix (NNDM). This method is implemented and tested on several datasets obtained from the UCI Machine Learning Repository and other datasets. The results demonstrate a significant impact on the predictive accuracy for feature selection combined with the supervised NNDM in classifying new instances. Therefore it can be used in other applications that require feature dimension reduction such as image and bioinformatics classifications.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133544555","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976509
Azuraini Abu Bakar, Choo-Yee Ting
Today, soft skills are crucial factors to the success of a project. For a certain set of jobs, soft skills are often considered more crucial than the hard skills or technical skills, in order to perform the job effectively. However, it is not a trivial task to identify the appropriate soft skills for each job. In this light, this study proposed a solution to assist employers when preparing advertisement via identification of suitable soft skills together with its relevancy to that particular job title. Bayesian network is employed to solve this problem because it is suitable for reasoning and decision making under uncertainty. The proposed Bayesian Network is trained using a dataset collected via extracting information from advertisements and also through interview sessions with a few identified experts.
{"title":"Soft skills recommendation systems for IT jobs: A Bayesian network approach","authors":"Azuraini Abu Bakar, Choo-Yee Ting","doi":"10.1109/DMO.2011.5976509","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976509","url":null,"abstract":"Today, soft skills are crucial factors to the success of a project. For a certain set of jobs, soft skills are often considered more crucial than the hard skills or technical skills, in order to perform the job effectively. However, it is not a trivial task to identify the appropriate soft skills for each job. In this light, this study proposed a solution to assist employers when preparing advertisement via identification of suitable soft skills together with its relevancy to that particular job title. Bayesian network is employed to solve this problem because it is suitable for reasoning and decision making under uncertainty. The proposed Bayesian Network is trained using a dataset collected via extracting information from advertisements and also through interview sessions with a few identified experts.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132000019","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976513
Sarina Sulaiman, Siti Mariyam Hj. Shamsuddin, A. Abraham
Web caching is a technology for improving network traffic on the internet. It is a temporary storage of Web objects (such as HTML documents) for later retrieval. There are three significant advantages to Web caching; reduced bandwidth consumption, reduced server load, and reduced latency. These rewards have made the Web less expensive with better performance. The aim of this research is to introduce advanced machine learning approaches for Web caching to decide either to cache or not to the cache server, which could be modelled as a classification problem. The challenges include identifying attributes ranking and significant improvements in the classification accuracy. Four methods are employed in this research; Classification and Regression Trees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF) and TreeNet (TN) are used for classification on Web caching. The experimental results reveal that CART performed extremely well in classifying Web objects from the existing log data and an excellent attribute to consider for an accomplishment of Web cache performance enhancement.
{"title":"Intelligent Web caching using Adaptive Regression Trees, Splines, Random Forests and Tree Net","authors":"Sarina Sulaiman, Siti Mariyam Hj. Shamsuddin, A. Abraham","doi":"10.1109/DMO.2011.5976513","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976513","url":null,"abstract":"Web caching is a technology for improving network traffic on the internet. It is a temporary storage of Web objects (such as HTML documents) for later retrieval. There are three significant advantages to Web caching; reduced bandwidth consumption, reduced server load, and reduced latency. These rewards have made the Web less expensive with better performance. The aim of this research is to introduce advanced machine learning approaches for Web caching to decide either to cache or not to the cache server, which could be modelled as a classification problem. The challenges include identifying attributes ranking and significant improvements in the classification accuracy. Four methods are employed in this research; Classification and Regression Trees (CART), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF) and TreeNet (TN) are used for classification on Web caching. The experimental results reveal that CART performed extremely well in classifying Web objects from the existing log data and an excellent attribute to consider for an accomplishment of Web cache performance enhancement.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133040425","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976499
Wan Muhammad Zulhafizsyam Wan Ahmad, S. Sulaiman, U. K. Yusof
The Internet contributes to the development of electronic community (e-community) portals. Such portals become an indispensable platform for members especially for a Special Interest Groups (SIG) to share knowledge and expertise in their respective fields. Finding expertise over the e-community portal will help interested people and researchers to identify other experts, working in the same area. However, it is quite a cumbersome task to search such expertise in the portal. In order to find an expert, expertise data mining could be a solution to ease the search of experts. Performing effective data mining technique will help to analyze and measure expertise level accurately in a SIG portal. This paper proposes a method called Expertise Data Mining (EDM) that comprises a few techniques for expertise search in a SIG portal. It expects to improve the finding of experts among the members of a SIG e-community.
{"title":"Data mining technique for expertise search in a special interest group knowledge portal","authors":"Wan Muhammad Zulhafizsyam Wan Ahmad, S. Sulaiman, U. K. Yusof","doi":"10.1109/DMO.2011.5976499","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976499","url":null,"abstract":"The Internet contributes to the development of electronic community (e-community) portals. Such portals become an indispensable platform for members especially for a Special Interest Groups (SIG) to share knowledge and expertise in their respective fields. Finding expertise over the e-community portal will help interested people and researchers to identify other experts, working in the same area. However, it is quite a cumbersome task to search such expertise in the portal. In order to find an expert, expertise data mining could be a solution to ease the search of experts. Performing effective data mining technique will help to analyze and measure expertise level accurately in a SIG portal. This paper proposes a method called Expertise Data Mining (EDM) that comprises a few techniques for expertise search in a SIG portal. It expects to improve the finding of experts among the members of a SIG e-community.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114219550","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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