Pub Date : 2012-10-15DOI: 10.1109/DMO.2012.6329792
Abbas Madraky, Z. Othman, Abdul Razak Hamdan
Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal data and performing data mining task easily while facing problem in frequently changing the data. In order to propose suitable data model, this research also investigate the analytical parameters, the structure and its specifications for Spatio-Temporal data. The concept of proposed data model is inspired from the nature of hair which has specific properties and its growth over the time. In order to have better looking and quality, the data is needed to maintain over the time such as combing, cutting, colouring, covering, cleaning etc. The proposed data model is represented by using mathematical model and later developed the data model tools. The data model is developed based on the existing relational and object-oriented models. This paper deals with the problems of available Spatio-Temporal data models for utilizing data mining technology and defines a new model based on analytical attributes and functions.
{"title":"Hair data model: A new data model for Spatio-Temporal data mining","authors":"Abbas Madraky, Z. Othman, Abdul Razak Hamdan","doi":"10.1109/DMO.2012.6329792","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329792","url":null,"abstract":"Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal data and performing data mining task easily while facing problem in frequently changing the data. In order to propose suitable data model, this research also investigate the analytical parameters, the structure and its specifications for Spatio-Temporal data. The concept of proposed data model is inspired from the nature of hair which has specific properties and its growth over the time. In order to have better looking and quality, the data is needed to maintain over the time such as combing, cutting, colouring, covering, cleaning etc. The proposed data model is represented by using mathematical model and later developed the data model tools. The data model is developed based on the existing relational and object-oriented models. This paper deals with the problems of available Spatio-Temporal data models for utilizing data mining technology and defines a new model based on analytical attributes and functions.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122924738","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329808
Abdullah S. Ghareb, A. Hamdan, A. Bakar
Text classification problem receives a lot of research that are based on machine learning, statistical, and information retrieval techniques. In the last decade, the associative classification algorithms which depends on pure data mining techniques appears as an effective method for classification. In this paper, we examine associative classification approach on the Arabic language to mine knowledge from Arabic text data set. Two methods of classification using AC are applied in this study; these methods are single rule prediction and multiple rule prediction. The experimental results against different classes of Arabic data set show that multiple rule prediction method outperforms single rule prediction method with regards to their accuracy. In general, the associative classification approach is a suitable method to classify Arabic text data set, and is able to achieve a good classification performance in terms of classification time and classification accuracy.
{"title":"Text associative classification approach for mining Arabic data set","authors":"Abdullah S. Ghareb, A. Hamdan, A. Bakar","doi":"10.1109/DMO.2012.6329808","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329808","url":null,"abstract":"Text classification problem receives a lot of research that are based on machine learning, statistical, and information retrieval techniques. In the last decade, the associative classification algorithms which depends on pure data mining techniques appears as an effective method for classification. In this paper, we examine associative classification approach on the Arabic language to mine knowledge from Arabic text data set. Two methods of classification using AC are applied in this study; these methods are single rule prediction and multiple rule prediction. The experimental results against different classes of Arabic data set show that multiple rule prediction method outperforms single rule prediction method with regards to their accuracy. In general, the associative classification approach is a suitable method to classify Arabic text data set, and is able to achieve a good classification performance in terms of classification time and classification accuracy.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125638035","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329811
Norlela Samsudin, Mazidah Puteh, A. Hamdan, M. Nazri
Opinion mining is used to automate the process of identifying opinion whether it is a positive or negative view. Majority of previous works on this field uses natural language programming techniques to identify the sentiment. This paper reports the use of artificial immune system (AIS) technique in identifying Malaysian online movie reviews. This opinion mining process uses three string similarity functions namely Cosine Similarity, Jaccard Coefficient and Sorensen Coefficient. In addition, AIS performance was compared with other traditional machine learning techniques, which are Support Vector Machine, Naïve Baiyes and k-Nearest Network. The result of the findings are analyzed and discussed in this paper.
{"title":"Is artificial immune system suitable for opinion mining?","authors":"Norlela Samsudin, Mazidah Puteh, A. Hamdan, M. Nazri","doi":"10.1109/DMO.2012.6329811","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329811","url":null,"abstract":"Opinion mining is used to automate the process of identifying opinion whether it is a positive or negative view. Majority of previous works on this field uses natural language programming techniques to identify the sentiment. This paper reports the use of artificial immune system (AIS) technique in identifying Malaysian online movie reviews. This opinion mining process uses three string similarity functions namely Cosine Similarity, Jaccard Coefficient and Sorensen Coefficient. In addition, AIS performance was compared with other traditional machine learning techniques, which are Support Vector Machine, Naïve Baiyes and k-Nearest Network. The result of the findings are analyzed and discussed in this paper.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141066","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329813
Sarinadia binti Safri, Nor Erne Nazira binti Bazin
Rapid innovation of products by manufacturers reduces the life cycle of the current product due to frequent introduction of new version of product to the market. Manufacturers dealing with these innovative products face problems to forecast the demand from customer, thus increase the cost of carrying the inventory of such products. Generally, this drawback is observed in highly demanded product, such as fashion goods and electronic devices (notebook, mobile phone). It is becoming more important for the manufacturer to adopt tools to manage their strategy in determining the suitable entry point for the new product. This paper presents a system dynamic approach for the conceptualization modeling of the factors influencing the introduction of new product within the shorter product life cycle. The conceptual diagram visualizes the relationships across the factors that contribute to the new product introduction is illustrated in causal loop diagram.
{"title":"Conceptualization of factors influencing new product introduction within shorter product life cycle","authors":"Sarinadia binti Safri, Nor Erne Nazira binti Bazin","doi":"10.1109/DMO.2012.6329813","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329813","url":null,"abstract":"Rapid innovation of products by manufacturers reduces the life cycle of the current product due to frequent introduction of new version of product to the market. Manufacturers dealing with these innovative products face problems to forecast the demand from customer, thus increase the cost of carrying the inventory of such products. Generally, this drawback is observed in highly demanded product, such as fashion goods and electronic devices (notebook, mobile phone). It is becoming more important for the manufacturer to adopt tools to manage their strategy in determining the suitable entry point for the new product. This paper presents a system dynamic approach for the conceptualization modeling of the factors influencing the introduction of new product within the shorter product life cycle. The conceptual diagram visualizes the relationships across the factors that contribute to the new product introduction is illustrated in causal loop diagram.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134372775","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329800
M. Y. SyarifahAdilah, R. Abdullah, I. Venkat
Mass spectrometry technique is gradually gaining momentum among the recent techniques deployed by several analytical research labs which intends to study biological or chemical properties of complex structures such as protein sequences. Literature reveals that reasoning voluminous mass spectrometry data via sophisticated computational techniques inspired by observing natural processes adapted by biological life has been yielding fruitful results towards the advancement of fields including bioinformatics and proteomics. Such advanced approaches provide efficient ways to mine mass spectrometry data in order to extract discriminating features that aid in discovering vital information, specifically discovering disease-related protein patterns in complex protein sequences. This study reveals the use of artificial bee colony (ABC) as a new feature selection technique incorporated with SVM classifier. Results achieved 96 and 100% for sensitivity and specificity respectively in discriminating cirrhosis and liver cancer cases.
{"title":"ABC algorithm as feature selection for biomarker discovery in mass spectrometry analysis","authors":"M. Y. SyarifahAdilah, R. Abdullah, I. Venkat","doi":"10.1109/DMO.2012.6329800","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329800","url":null,"abstract":"Mass spectrometry technique is gradually gaining momentum among the recent techniques deployed by several analytical research labs which intends to study biological or chemical properties of complex structures such as protein sequences. Literature reveals that reasoning voluminous mass spectrometry data via sophisticated computational techniques inspired by observing natural processes adapted by biological life has been yielding fruitful results towards the advancement of fields including bioinformatics and proteomics. Such advanced approaches provide efficient ways to mine mass spectrometry data in order to extract discriminating features that aid in discovering vital information, specifically discovering disease-related protein patterns in complex protein sequences. This study reveals the use of artificial bee colony (ABC) as a new feature selection technique incorporated with SVM classifier. Results achieved 96 and 100% for sensitivity and specificity respectively in discriminating cirrhosis and liver cancer cases.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121912491","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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