Pub Date : 2011-04-11DOI: 10.1109/CIDM.2011.5949299
Corrado Loglisci, Michelangelo Ceci, A. Appice, D. Malerba
The automatic discovery of process models can help to gain insight into various perspectives (e.g., control flow or data perspective) of the process executions traced in an event log. Frequent patterns mining offers a means to build human understandable representations of these process models. This paper describes the application of a multi-relational method of frequent pattern discovery into process mining. Multi-relational data mining is demanded for the variety of activities and actors involved in the process executions traced in an event log which leads to a relational (or structural) representation of the process executions. Peculiarity of this work is in the integration of disjunctive forms into relational patterns discovered from event logs. The introduction of disjunctive forms enables relational patterns to express frequent variants of process models. The effectiveness of using relational patterns with disjunctions to describe process models with variants is assessed on real logs of process executions.
{"title":"Discovering process models through relational disjunctive patterns mining","authors":"Corrado Loglisci, Michelangelo Ceci, A. Appice, D. Malerba","doi":"10.1109/CIDM.2011.5949299","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949299","url":null,"abstract":"The automatic discovery of process models can help to gain insight into various perspectives (e.g., control flow or data perspective) of the process executions traced in an event log. Frequent patterns mining offers a means to build human understandable representations of these process models. This paper describes the application of a multi-relational method of frequent pattern discovery into process mining. Multi-relational data mining is demanded for the variety of activities and actors involved in the process executions traced in an event log which leads to a relational (or structural) representation of the process executions. Peculiarity of this work is in the integration of disjunctive forms into relational patterns discovered from event logs. The introduction of disjunctive forms enables relational patterns to express frequent variants of process models. The effectiveness of using relational patterns with disjunctions to describe process models with variants is assessed on real logs of process executions.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124117550","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949421
Tobias Reitmaier, B. Sick
In this article, we introduce and investigate 3DS, a novel selection strategy for pool-based active training of a generative classifier, namely CMM (classifier based on a probabilistic mixture model). Such a generative classifier aims at modeling the processes underlying the “generation” of the data. The strategy 3DS considers the distance of samples to the decision boundary, the density in regions where samples are selected, and the diversity of samples in the query set that are chosen for labeling, e.g., by a human domain expert. The combination of the three measures in 3DS is adaptive in the sense that the weights of the distance and the density measure depend on the uniqueness of the classification. With nine benchmark data sets it is shown that 3DS outperforms a random selection strategy (baseline method), a pure closest sampling approach, ITDS (information theoretic diversity sampling), DWUS (density-weighted uncertainty sampling), DUAL (dual strategy for active learning), and PBAC (prototype based active learning) regarding evaluation criteria such as ranked performance based on classification accuracy, number of labeled samples (data utilization), and learning speed assessed by the area under the learning curve.
{"title":"Active classifier training with the 3DS strategy","authors":"Tobias Reitmaier, B. Sick","doi":"10.1109/CIDM.2011.5949421","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949421","url":null,"abstract":"In this article, we introduce and investigate 3DS, a novel selection strategy for pool-based active training of a generative classifier, namely CMM (classifier based on a probabilistic mixture model). Such a generative classifier aims at modeling the processes underlying the “generation” of the data. The strategy 3DS considers the distance of samples to the decision boundary, the density in regions where samples are selected, and the diversity of samples in the query set that are chosen for labeling, e.g., by a human domain expert. The combination of the three measures in 3DS is adaptive in the sense that the weights of the distance and the density measure depend on the uniqueness of the classification. With nine benchmark data sets it is shown that 3DS outperforms a random selection strategy (baseline method), a pure closest sampling approach, ITDS (information theoretic diversity sampling), DWUS (density-weighted uncertainty sampling), DUAL (dual strategy for active learning), and PBAC (prototype based active learning) regarding evaluation criteria such as ranked performance based on classification accuracy, number of labeled samples (data utilization), and learning speed assessed by the area under the learning curve.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114373306","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949431
R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme
Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.
{"title":"Active learning for aspect model in recommender systems","authors":"R. Karimi, C. Freudenthaler, A. Nanopoulos, L. Schmidt-Thieme","doi":"10.1109/CIDM.2011.5949431","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949431","url":null,"abstract":"Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130867232","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949432
A. Ghanbari, S. Abbasian-Naghneh, E. Hadavandi
Computational intelligence (CI) as an offshoot of artificial intelligence (AI), is becoming more and more widespread nowadays for solving different engineering problems. Especially by embracing Swarm Intelligence techniques such as ant colony optimization (ACO), CI is known as a good alternative to classical AI for dealing with practical problems which are not easy to solve by traditional methods. Besides, electricity load forecasting is one of the most important concerns of power systems, consequently; developing intelligent methods in order to perform accurate forecasts is vital for such systems. This study presents a hybrid CI methodology (called ACO-GA) by integration of ant colony optimization, genetic algorithm (GA) and fuzzy logic to construct a load forecasting expert system. The superiority and applicability of ACO-GA is shown for Iran's annual electricity load forecasting problem and results are compared with adaptive neuro-fuzzy inference system (ANFIS), which is a common approach in this field. The outcomes indicate that ACO-GA provides more accurate results than ANFIS approach. Moreover, the results of this study provide decision makers with an appropriate simulation tool to make more accurate forecasts on future electricity loads.
{"title":"An intelligent load forecasting expert system by integration of ant colony optimization, genetic algorithms and fuzzy logic","authors":"A. Ghanbari, S. Abbasian-Naghneh, E. Hadavandi","doi":"10.1109/CIDM.2011.5949432","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949432","url":null,"abstract":"Computational intelligence (CI) as an offshoot of artificial intelligence (AI), is becoming more and more widespread nowadays for solving different engineering problems. Especially by embracing Swarm Intelligence techniques such as ant colony optimization (ACO), CI is known as a good alternative to classical AI for dealing with practical problems which are not easy to solve by traditional methods. Besides, electricity load forecasting is one of the most important concerns of power systems, consequently; developing intelligent methods in order to perform accurate forecasts is vital for such systems. This study presents a hybrid CI methodology (called ACO-GA) by integration of ant colony optimization, genetic algorithm (GA) and fuzzy logic to construct a load forecasting expert system. The superiority and applicability of ACO-GA is shown for Iran's annual electricity load forecasting problem and results are compared with adaptive neuro-fuzzy inference system (ANFIS), which is a common approach in this field. The outcomes indicate that ACO-GA provides more accurate results than ANFIS approach. Moreover, the results of this study provide decision makers with an appropriate simulation tool to make more accurate forecasts on future electricity loads.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126822695","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949453
A. Weijters, J. Ribeiro
One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain nontrivial constructs, processes that are low structured and/or dealing with the presence of noise in the event logs. To overcome these problems, a new process representation language is presented in combination with an accompanying process mining algorithm. The most significant property of the new representation language is in the way the semantics of splits and joins are represented; by using so-called split/join frequency tables. This results in easy to understand process models even in the case of non-trivial constructs, low structured domains and the presence of noise. This paper explains the new process representation language and how the mining algorithm works. The algorithm is implemented as a plug-in in the ProM framework. An illustrative example with noise and a real life log of a complex and low structured process are used to explicate the presented approach.
{"title":"Flexible Heuristics Miner (FHM)","authors":"A. Weijters, J. Ribeiro","doi":"10.1109/CIDM.2011.5949453","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949453","url":null,"abstract":"One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain nontrivial constructs, processes that are low structured and/or dealing with the presence of noise in the event logs. To overcome these problems, a new process representation language is presented in combination with an accompanying process mining algorithm. The most significant property of the new representation language is in the way the semantics of splits and joins are represented; by using so-called split/join frequency tables. This results in easy to understand process models even in the case of non-trivial constructs, low structured domains and the presence of noise. This paper explains the new process representation language and how the mining algorithm works. The algorithm is implemented as a plug-in in the ProM framework. An illustrative example with noise and a real life log of a complex and low structured process are used to explicate the presented approach.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126572411","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949301
José M. Carmona-Cejudo, Manuel Baena-García, J. D. Campo-Ávila, Rafael Morales Bueno
Multi-label learning is a very interesting field in Machine Learning. It allows to generalise standard methods and evaluation procedures, and tackle challenging real problems where one example can be tagged with more than one label. In this paper we study the performance of different multi-label methods in combination with standard single-label algorithms, using several specific multi-label metrics. What we want to show is how a good preprocessing phase can improve the performance of such methods and algorithms. As we will explain, its main advantage is a shorter time to induce the models, while keeping (even improving) other classification quality measures. We use the GNUsmail framework to do the preprocessing of an existing and extensively used dataset, to obtain a reduced feature space that conserves the relevant information and allows improvements on performance. Thanks to the capabilities of GNUsmail, the preprocessing step can be easily applied to different email datasets.
{"title":"Feature extraction for multi-label learning in the domain of email classification","authors":"José M. Carmona-Cejudo, Manuel Baena-García, J. D. Campo-Ávila, Rafael Morales Bueno","doi":"10.1109/CIDM.2011.5949301","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949301","url":null,"abstract":"Multi-label learning is a very interesting field in Machine Learning. It allows to generalise standard methods and evaluation procedures, and tackle challenging real problems where one example can be tagged with more than one label. In this paper we study the performance of different multi-label methods in combination with standard single-label algorithms, using several specific multi-label metrics. What we want to show is how a good preprocessing phase can improve the performance of such methods and algorithms. As we will explain, its main advantage is a shorter time to induce the models, while keeping (even improving) other classification quality measures. We use the GNUsmail framework to do the preprocessing of an existing and extensively used dataset, to obtain a reduced feature space that conserves the relevant information and allows improvements on performance. Thanks to the capabilities of GNUsmail, the preprocessing step can be easily applied to different email datasets.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115259954","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949456
A. Fonseka, D. Alahakoon, S. Bedingfield
A significant problem which arises during the process of knowledge discovery is dealing with data which have temporal dependencies. The attributes associated with temporal data need to be processed differently from non temporal attributes. A typical approach to address this issue is to view temporal data as an ordered sequence of events. In this work, we propose a novel dynamic unsupervised learning approach to discover patterns in temporal data. The new technique is based on the Growing Self-Organization Map (GSOM), which is a structure adapting version of the Self-Organizing Map (SOM). The SOM is widely used in knowledge discovery applications due to its unsupervised learning nature, ease of use and visualization capabilities. The GSOM further enhances the SOM with faster processing, more representative cluster formation and the ability to control map spread. This paper describes a significant extension to the GSOM enabling it to be used to for analyzing data with temporal sequences. The similarity between two time dependent sequences with unequal length is estimated using the Dynamic Time Warping (DTW) algorithm incorporated into the GSOM. Experiments were carried out to evaluate the performance and the validity of the proposed approach using an audio-visual data set. The results demonstrate that the novel “GSOM Sequence” algorithm improves the accuracy and validity of the clusters obtained.
{"title":"GSOM sequence: An unsupervised dynamic approach for knowledge discovery in temporal data","authors":"A. Fonseka, D. Alahakoon, S. Bedingfield","doi":"10.1109/CIDM.2011.5949456","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949456","url":null,"abstract":"A significant problem which arises during the process of knowledge discovery is dealing with data which have temporal dependencies. The attributes associated with temporal data need to be processed differently from non temporal attributes. A typical approach to address this issue is to view temporal data as an ordered sequence of events. In this work, we propose a novel dynamic unsupervised learning approach to discover patterns in temporal data. The new technique is based on the Growing Self-Organization Map (GSOM), which is a structure adapting version of the Self-Organizing Map (SOM). The SOM is widely used in knowledge discovery applications due to its unsupervised learning nature, ease of use and visualization capabilities. The GSOM further enhances the SOM with faster processing, more representative cluster formation and the ability to control map spread. This paper describes a significant extension to the GSOM enabling it to be used to for analyzing data with temporal sequences. The similarity between two time dependent sequences with unequal length is estimated using the Dynamic Time Warping (DTW) algorithm incorporated into the GSOM. Experiments were carried out to evaluate the performance and the validity of the proposed approach using an audio-visual data set. The results demonstrate that the novel “GSOM Sequence” algorithm improves the accuracy and validity of the clusters obtained.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130122164","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949442
Pablo Escandell-Montero, J. Martínez-Martínez, J. Martín-Guerrero, E. Soria-Olivas, J. Vila-Francés, J. R. M. Benedito
The aim of this work is to study the applicability of reinforcement learning methods to design adaptive treatment strategies that optimize, in the long-term, the dosage of erythropoiesis-stimulating agents (ESAs) in the management of anemia in patients undergoing hemodialysis. Adaptive treatment strategies are recently emerging as a new paradigm for the treatment and long-term management of the chronic disease. Reinforcement Learning (RL) can be useful to extract such strategies from clinical data, taking into account delayed effects and without requiring any mathematical model. In this work, we focus on the so-called Fitted Q Iteration algorithm, a RL approach that deals with the data very efficiently. Achieved results show the suitability of the proposed RL policies that can improve the performance of the treatment followed in the clinics. The methodology can be easily extended to other problems of drug dosage optimization.
{"title":"Adaptive treatment of anemia on hemodialysis patients: A reinforcement learning approach","authors":"Pablo Escandell-Montero, J. Martínez-Martínez, J. Martín-Guerrero, E. Soria-Olivas, J. Vila-Francés, J. R. M. Benedito","doi":"10.1109/CIDM.2011.5949442","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949442","url":null,"abstract":"The aim of this work is to study the applicability of reinforcement learning methods to design adaptive treatment strategies that optimize, in the long-term, the dosage of erythropoiesis-stimulating agents (ESAs) in the management of anemia in patients undergoing hemodialysis. Adaptive treatment strategies are recently emerging as a new paradigm for the treatment and long-term management of the chronic disease. Reinforcement Learning (RL) can be useful to extract such strategies from clinical data, taking into account delayed effects and without requiring any mathematical model. In this work, we focus on the so-called Fitted Q Iteration algorithm, a RL approach that deals with the data very efficiently. Achieved results show the suitability of the proposed RL policies that can improve the performance of the treatment followed in the clinics. The methodology can be easily extended to other problems of drug dosage optimization.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115441011","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949455
A. Takasu, Saranya Maneeroj
This paper proposes an algorithm for recommender systems that uses both positive and negative latent user models. In recommending items to a user, recommender systems usually exploit item content information as well as the preferences of similar users. Various types of content information can be attached to items and these are useful for judging user preferences. For example, in movie recommendations, a movie record may include the director, the actors, and reviews. These types of information help systems calculate sophisticated user preferences. We first propose a probabilistic model that maps multi-attributed records into a low-dimensional feature space. The proposed model extends latent Dirichlet allocation to the handling of multi-attributed data. We derive an algorithm for estimating the model's parameters using the Gibbs sampling technique. Next, we propose a probabilistic model to calculate user preferences for items in the feature space. Finally, we develop a recommendation algorithm based on the probabilistic model that works efficiently for large quantities of items and user ratings. We use a publicly available movie corpus to evaluate the proposed algorithm empirically, in terms of both its recommendation accuracy and its processing efficiency.
{"title":"A recommendation algorithm using positive and negative latent models","authors":"A. Takasu, Saranya Maneeroj","doi":"10.1109/CIDM.2011.5949455","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949455","url":null,"abstract":"This paper proposes an algorithm for recommender systems that uses both positive and negative latent user models. In recommending items to a user, recommender systems usually exploit item content information as well as the preferences of similar users. Various types of content information can be attached to items and these are useful for judging user preferences. For example, in movie recommendations, a movie record may include the director, the actors, and reviews. These types of information help systems calculate sophisticated user preferences. We first propose a probabilistic model that maps multi-attributed records into a low-dimensional feature space. The proposed model extends latent Dirichlet allocation to the handling of multi-attributed data. We derive an algorithm for estimating the model's parameters using the Gibbs sampling technique. Next, we propose a probabilistic model to calculate user preferences for items in the feature space. Finally, we develop a recommendation algorithm based on the probabilistic model that works efficiently for large quantities of items and user ratings. We use a publicly available movie corpus to evaluate the proposed algorithm empirically, in terms of both its recommendation accuracy and its processing efficiency.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124840477","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 : 2011-04-11DOI: 10.1109/CIDM.2011.5949433
Peter Sarlin
The Millennium Development Goals (MDGs) represent commitments to reduce poverty and hunger, and to tackle ill-health, gender inequality, lack of education, lack of access to clean water and environmental degradation by 2015. The eight goals of the Millennium Declaration are tracked using 21 benchmark targets, measured by 60 indicators. This paper explores whether the application of the Self-organizing map (SOM), a neural network-based projection and clustering technique, facilitates monitoring of the multidimensional MDGs. First, this paper presents a SOM model for visual benchmarking of countries and for visual analysis of the evolution of MDG indicators. Second, the SOM is paired with a geospatial dimension by mapping the clustering results on a geographic map. The results of this paper indicate that the SOM is a feasible tool for visual monitoring of MDG indicators.
{"title":"Visual tracking of the Millennium Development Goals with a Self-organizing neural network","authors":"Peter Sarlin","doi":"10.1109/CIDM.2011.5949433","DOIUrl":"https://doi.org/10.1109/CIDM.2011.5949433","url":null,"abstract":"The Millennium Development Goals (MDGs) represent commitments to reduce poverty and hunger, and to tackle ill-health, gender inequality, lack of education, lack of access to clean water and environmental degradation by 2015. The eight goals of the Millennium Declaration are tracked using 21 benchmark targets, measured by 60 indicators. This paper explores whether the application of the Self-organizing map (SOM), a neural network-based projection and clustering technique, facilitates monitoring of the multidimensional MDGs. First, this paper presents a SOM model for visual benchmarking of countries and for visual analysis of the evolution of MDG indicators. Second, the SOM is paired with a geospatial dimension by mapping the clustering results on a geographic map. The results of this paper indicate that the SOM is a feasible tool for visual monitoring of MDG indicators.","PeriodicalId":211565,"journal":{"name":"2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120937363","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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