The Experts below are selected from a list of 8067 Experts worldwide ranked by ideXlab platform
Sudha Subramani - One of the best experts on this subject based on the ideXlab platform.
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Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media
arXiv: Information Retrieval, 2018Co-Authors: Sudha Subramani, Manjula O'connorAbstract:Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care. However there is less attention given on social welfare issues like DV and its impact on public health. Recently, the victims of DV turned to social media platforms to express their feelings in the form of posts and seek the social and emotional support, for sympathetic encouragement, to show compassion and empathy among public. But, it is difficult to mine the Actionable Knowledge from large conversational datasets from social media due to the characteristics of high dimensions, short, noisy, huge volume, high velocity, and so on. Hence, this paper will propose a novel framework to model and discover the various themes related to DV from the public domain. The proposed framework would possibly provide unprecedentedly valuable information to the public health researchers, national family health organizations, government and public with data enrichment and consolidation to improve the social welfare of the community. Thus provides Actionable Knowledge by monitoring and analysing continuous and rich user generated content.
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mining Actionable Knowledge using reordering based diversified Actionable decision trees
Web Information Systems Engineering, 2016Co-Authors: Sudha Subramani, Hua Wang, Sathiyabhama Balasubramaniam, Rui Zhou, Yanchun Zhang, Frank Whittaker, Yueai Zhao, Sarathkumar RangarajanAbstract:Actionable Knowledge discovery plays a vital role in industrial problems such as Customer Relationship Management, insurance and banking. Actionable Knowledge discovery techniques are not only useful in pointing out customers who are loyal and likely attritors, but it also suggests actions to transform customers from undesirable to desirable. Postprocessing is one of the Actionable Knowledge discovery techniques which are efficient and effective in strategic decision making and used to unearth hidden patterns and unknown correlations underlying the business data. In this paper, we present a novel technique named Reordering based Diversified Actionable Decision Trees (RDADT), which is an effective Actionable Knowledge discovery based classification algorithm. RDADT contrasts traditional classification algorithms by constructing committees of decision trees in a reordered fashion and discover Actionable rules containing all the attributes. Experimental evaluation on UCI benchmark data shows that the proposed technique has higher classification accuracy than traditional decision tree algorithms.
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WISE (1) - Mining Actionable Knowledge Using Reordering Based Diversified Actionable Decision Trees
Web Information Systems Engineering – WISE 2016, 2016Co-Authors: Sudha Subramani, Hua Wang, Sathiyabhama Balasubramaniam, Rui Zhou, Yanchun Zhang, Frank Whittaker, Yueai Zhao, Sarathkumar RangarajanAbstract:Actionable Knowledge discovery plays a vital role in industrial problems such as Customer Relationship Management, insurance and banking. Actionable Knowledge discovery techniques are not only useful in pointing out customers who are loyal and likely attritors, but it also suggests actions to transform customers from undesirable to desirable. Postprocessing is one of the Actionable Knowledge discovery techniques which are efficient and effective in strategic decision making and used to unearth hidden patterns and unknown correlations underlying the business data. In this paper, we present a novel technique named Reordering based Diversified Actionable Decision Trees (RDADT), which is an effective Actionable Knowledge discovery based classification algorithm. RDADT contrasts traditional classification algorithms by constructing committees of decision trees in a reordered fashion and discover Actionable rules containing all the attributes. Experimental evaluation on UCI benchmark data shows that the proposed technique has higher classification accuracy than traditional decision tree algorithms.
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ADCONS - Post mining of diversified multiple decision trees for Actionable Knowledge discovery
Lecture Notes in Computer Science, 2012Co-Authors: Sudha Subramani, Sathiyabhama BalasubramaniamAbstract:Most data mining algorithms and tools when applied to industrial problems such as Customer Relationship Management, insurance and banking they stop search at producing actual applicable Knowledge. Unlike these models, Actionable Knowledge discovery techniques are useful in pointing out customers who are likely attritors and loyal. However, Actionable Knowledge discovery techniques require human experts to postprocess the discovered Knowledge manually. Postprocessing is one of the Actionable Knowledge discovery techniques which are effective in decision making and overcomes considerable inefficiency which leads to human errors that are inherent in the traditional data mining systems. Hence, decision trees are postprocessed which suggest cost effective actions in order to maximize the profit based objective function. In the proposed approach, an effective Actionable Knowledge discovery based classification algorithm namely Actionable Multiple Decision Trees (AMDT) is developed to improve the robustness and classification accuracy and tests are conducted on UCI German benchmark data.
Qiang Yang - One of the best experts on this subject based on the ideXlab platform.
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mining web logs for Actionable Knowledge
Intelligent Technologies for Information Analysis, 2004Co-Authors: Qiang Yang, Charles X Ling, Jianfeng GaoAbstract:Everyday, popular Websites attract millions of visitors. These visitors leave behind vast amounts of Websites traversal information in the form of Web server and query logs. By analyzing these logs, it is possible to discover various kinds of Knowledge, which can be applied to improve the performance of Web services. A particularly useful kind of Knowledge is Knowledge that can be immediately applied to the operation of the Web-sites; we call this type of Knowledge Actionable Knowledge. In this chapter, we present three examples of Actionable Web log mining. The first method is to mine a Web log for Markov models that can be used for improving caching and prefetching of Web objects. A second method is to use the mined Knowledge for building better, adaptive user interfaces. The new user interface can adjust as the user behavior changes with time. Finally, we present an example of applying Web query log Knowledge to improving Web search for a search engine application.
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Guest Editors' Introduction: Mining Actionable Knowledge on the Web
IEEE Intelligent Systems, 2004Co-Authors: Qiang Yang, Craig A. KnoblockAbstract:The Web-its resources and users-offers a wealth of information for data mining and Knowledge discovery. Up to now, a great deal of work has been done applying data mining and machine learning methods to discover novel and useful Knowledge on the Web. However, many techniques aim only at extracting Knowledge for human users to view and use. Recently, more and more work addresses Web for Knowledge that computer systems will use. You can apply such Actionable Knowledge back to the Web for measurable performance improvements. This special issue of IEEE Intelligent Systems features five articles that address the problem of Actionable Web mining.
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Intelligent Technologies for Information Analysis - Mining web logs for Actionable Knowledge
Intelligent Technologies for Information Analysis, 2004Co-Authors: Qiang Yang, Charles X Ling, Jianfeng GaoAbstract:Everyday, popular Websites attract millions of visitors. These visitors leave behind vast amounts of Websites traversal information in the form of Web server and query logs. By analyzing these logs, it is possible to discover various kinds of Knowledge, which can be applied to improve the performance of Web services. A particularly useful kind of Knowledge is Knowledge that can be immediately applied to the operation of the Web-sites; we call this type of Knowledge Actionable Knowledge. In this chapter, we present three examples of Actionable Web log mining. The first method is to mine a Web log for Markov models that can be used for improving caching and prefetching of Web objects. A second method is to use the mined Knowledge for building better, adaptive user interfaces. The new user interface can adjust as the user behavior changes with time. Finally, we present an example of applying Web query log Knowledge to improving Web search for a search engine application.
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postprocessing decision trees to extract Actionable Knowledge
International Conference on Data Mining, 2003Co-Authors: Qiang Yang, Charles X Ling, Jie Yin, T ChenAbstract:Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the mined information manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase the objective function such as profit. Here, we present a novel algorithm that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing objective function: the expected net profit. We develop these algorithms under resource constraints that are abound in reality. The contribution of the work is in taking the output from an existing mature technique (decision trees, for example), and producing novel, Actionable Knowledge through automatic postprocessing.
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Postprocessing decision trees to extract Actionable Knowledge
2003Co-Authors: Qiang Yang, Jie YinAbstract:Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the mined information manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase the objective function such as profit. In this paper, we present a novel algorithm that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing objective function: the expected net profit. We develop these algorithms under resource constraints that are abound in reality. The contribution of the work is in taking the output from an existing mature technique (decision trees, for example), and producing novel, Actionable Knowledge through automatic postprocessing. 1
Sathiyabhama Balasubramaniam - One of the best experts on this subject based on the ideXlab platform.
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mining Actionable Knowledge using reordering based diversified Actionable decision trees
Web Information Systems Engineering, 2016Co-Authors: Sudha Subramani, Hua Wang, Sathiyabhama Balasubramaniam, Rui Zhou, Yanchun Zhang, Frank Whittaker, Yueai Zhao, Sarathkumar RangarajanAbstract:Actionable Knowledge discovery plays a vital role in industrial problems such as Customer Relationship Management, insurance and banking. Actionable Knowledge discovery techniques are not only useful in pointing out customers who are loyal and likely attritors, but it also suggests actions to transform customers from undesirable to desirable. Postprocessing is one of the Actionable Knowledge discovery techniques which are efficient and effective in strategic decision making and used to unearth hidden patterns and unknown correlations underlying the business data. In this paper, we present a novel technique named Reordering based Diversified Actionable Decision Trees (RDADT), which is an effective Actionable Knowledge discovery based classification algorithm. RDADT contrasts traditional classification algorithms by constructing committees of decision trees in a reordered fashion and discover Actionable rules containing all the attributes. Experimental evaluation on UCI benchmark data shows that the proposed technique has higher classification accuracy than traditional decision tree algorithms.
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WISE (1) - Mining Actionable Knowledge Using Reordering Based Diversified Actionable Decision Trees
Web Information Systems Engineering – WISE 2016, 2016Co-Authors: Sudha Subramani, Hua Wang, Sathiyabhama Balasubramaniam, Rui Zhou, Yanchun Zhang, Frank Whittaker, Yueai Zhao, Sarathkumar RangarajanAbstract:Actionable Knowledge discovery plays a vital role in industrial problems such as Customer Relationship Management, insurance and banking. Actionable Knowledge discovery techniques are not only useful in pointing out customers who are loyal and likely attritors, but it also suggests actions to transform customers from undesirable to desirable. Postprocessing is one of the Actionable Knowledge discovery techniques which are efficient and effective in strategic decision making and used to unearth hidden patterns and unknown correlations underlying the business data. In this paper, we present a novel technique named Reordering based Diversified Actionable Decision Trees (RDADT), which is an effective Actionable Knowledge discovery based classification algorithm. RDADT contrasts traditional classification algorithms by constructing committees of decision trees in a reordered fashion and discover Actionable rules containing all the attributes. Experimental evaluation on UCI benchmark data shows that the proposed technique has higher classification accuracy than traditional decision tree algorithms.
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ADCONS - Post mining of diversified multiple decision trees for Actionable Knowledge discovery
Lecture Notes in Computer Science, 2012Co-Authors: Sudha Subramani, Sathiyabhama BalasubramaniamAbstract:Most data mining algorithms and tools when applied to industrial problems such as Customer Relationship Management, insurance and banking they stop search at producing actual applicable Knowledge. Unlike these models, Actionable Knowledge discovery techniques are useful in pointing out customers who are likely attritors and loyal. However, Actionable Knowledge discovery techniques require human experts to postprocess the discovered Knowledge manually. Postprocessing is one of the Actionable Knowledge discovery techniques which are effective in decision making and overcomes considerable inefficiency which leads to human errors that are inherent in the traditional data mining systems. Hence, decision trees are postprocessed which suggest cost effective actions in order to maximize the profit based objective function. In the proposed approach, an effective Actionable Knowledge discovery based classification algorithm namely Actionable Multiple Decision Trees (AMDT) is developed to improve the robustness and classification accuracy and tests are conducted on UCI German benchmark data.
Eynollah Khanjari - One of the best experts on this subject based on the ideXlab platform.
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Actionable Knowledge discovery from social networks using causal structures of structural features
Journal of Intelligent & Fuzzy Systems, 2020Co-Authors: Nasrin Kalanat, Alireza Khanshan, Eynollah KhanjariAbstract:Knowledge discovery and data mining provide an array of solutions for real-world problems. When facing business requirements, the ultimate goal of Knowledge discovery is not the Knowledge itself but rather making the gained Knowledge practical. Consequently, the models and patterns found by the mining methods often require post-processing. To this end, Actionable Knowledge discovery has been introduced which is developed to extract Actionable Knowledge from data. The output of Actionable Knowledge discovery is a set of actions that help the domain expert to gain the desired outcome. Such a process where a set of actions are extracted is called action extraction. One of the challenges of action extraction is to incorporate causal dependencies among the variables to find actions with higher effectiveness compared to when no such dependencies are used. The goal of this paper is to dive into the lesser studied subject of “action discovery in social networks” and intends to extract actions by utilizing the casual structures discovered from such data. Furthermore, in order to capture the underlying information within a social network, we extract the corresponding structural features. We propose a method called SF-ICE-CREAM (Social Features included Inductive Causation Enabled Causal Relationship-based Economical Action Mining) to overcome the challenges introduced above. This method uses structural features to find the underlying causal structures within a social network and incorporates them into the action extraction process.
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Extracting Actionable Knowledge From Social Networks Using Structural Features
IEEE Access, 2020Co-Authors: Nasrin Kalanat, Eynollah Khanjari, Alireza KhanshanAbstract:In conventional data mining methods, the output is either a description of input data or a prediction of unseen data. But the real-world problems usually require interventions in order to alter the current data specifications towards a desirable goal. Actionable Knowledge discovery is a field of study specifically developed for this matter. Existing methods rarely tackled the problem of extracting Actionable Knowledge from social networks. Moreover, due to the dependencies among the underlying network data, extracted actions should be evaluated since the changes suggested by the actions may not be described by the model constructed so far. This enforces the refinement of the model to preserve the quality of extracted actions. In this paper we propose a new method for action mining which incorporates an action evaluation process overcoming the mentioned problem while focusing specifically on social network data. Such data contains valuable information based on the links inside the network where a change in some feature values may result in a chain of changes in others due to the dependencies conveyed by the links in the network. We use a state-of-the-art structural feature extraction method to capture the information of the dependencies inside the network. Our proposed method iteratively updates structural features which are incorporated in the action extraction process. In this process, we thoroughly examine the effects of the application of actions by discovering the impact of possible changes in the network. We call this phenomenon “change propagation”. According to our experimentations, our method outperforms the state-of-the-art methods in terms of action effectiveness and reliability with comparable efficiency.
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Extracting Actionable Knowledge from social networks with node attributes
Expert Systems with Applications, 2020Co-Authors: Nasrin Kalanat, Eynollah KhanjariAbstract:Abstract Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable Knowledge in each specific domain. An action is a new tool in this research area that suggests some changes to the user to gain a profit in his/her domain. Currently, most of action mining methods rely on simple data which describes each object independently. Since social data has more complex structure due to the relationships between individuals, a major problem is that such structural information is not taken into account in the action mining process. This leads to miss some useful Knowledge and profitable actions. Consequently, more effective methods are needed for mining actions. The main focus of this work is to extract cost-effective actions from social networks in which nodes have attributes. The actions suggest optimal changes in nodes’ attributes that are likely to result in changing labels of users to more desired one when they are applied. We develop an action mining method based on Random Walks that naturally combines the information from the network structure with nodes attributes. We formulate action mining as an optimization problem where the goal is to learn a function that varies the values of nodes’ attributes which in turn affect edges’ weights in the network so that the labels of intended individuals are likely to take the desired label while minimizing the cost of incurring the changes. Experiments confirm that the proposed approach outperforms the current state-of-the-art in action mining.
Nasrin Kalanat - One of the best experts on this subject based on the ideXlab platform.
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Actionable Knowledge discovery from social networks using causal structures of structural features
Journal of Intelligent & Fuzzy Systems, 2020Co-Authors: Nasrin Kalanat, Alireza Khanshan, Eynollah KhanjariAbstract:Knowledge discovery and data mining provide an array of solutions for real-world problems. When facing business requirements, the ultimate goal of Knowledge discovery is not the Knowledge itself but rather making the gained Knowledge practical. Consequently, the models and patterns found by the mining methods often require post-processing. To this end, Actionable Knowledge discovery has been introduced which is developed to extract Actionable Knowledge from data. The output of Actionable Knowledge discovery is a set of actions that help the domain expert to gain the desired outcome. Such a process where a set of actions are extracted is called action extraction. One of the challenges of action extraction is to incorporate causal dependencies among the variables to find actions with higher effectiveness compared to when no such dependencies are used. The goal of this paper is to dive into the lesser studied subject of “action discovery in social networks” and intends to extract actions by utilizing the casual structures discovered from such data. Furthermore, in order to capture the underlying information within a social network, we extract the corresponding structural features. We propose a method called SF-ICE-CREAM (Social Features included Inductive Causation Enabled Causal Relationship-based Economical Action Mining) to overcome the challenges introduced above. This method uses structural features to find the underlying causal structures within a social network and incorporates them into the action extraction process.
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Extracting Actionable Knowledge From Social Networks Using Structural Features
IEEE Access, 2020Co-Authors: Nasrin Kalanat, Eynollah Khanjari, Alireza KhanshanAbstract:In conventional data mining methods, the output is either a description of input data or a prediction of unseen data. But the real-world problems usually require interventions in order to alter the current data specifications towards a desirable goal. Actionable Knowledge discovery is a field of study specifically developed for this matter. Existing methods rarely tackled the problem of extracting Actionable Knowledge from social networks. Moreover, due to the dependencies among the underlying network data, extracted actions should be evaluated since the changes suggested by the actions may not be described by the model constructed so far. This enforces the refinement of the model to preserve the quality of extracted actions. In this paper we propose a new method for action mining which incorporates an action evaluation process overcoming the mentioned problem while focusing specifically on social network data. Such data contains valuable information based on the links inside the network where a change in some feature values may result in a chain of changes in others due to the dependencies conveyed by the links in the network. We use a state-of-the-art structural feature extraction method to capture the information of the dependencies inside the network. Our proposed method iteratively updates structural features which are incorporated in the action extraction process. In this process, we thoroughly examine the effects of the application of actions by discovering the impact of possible changes in the network. We call this phenomenon “change propagation”. According to our experimentations, our method outperforms the state-of-the-art methods in terms of action effectiveness and reliability with comparable efficiency.
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Extracting Actionable Knowledge from social networks with node attributes
Expert Systems with Applications, 2020Co-Authors: Nasrin Kalanat, Eynollah KhanjariAbstract:Abstract Actionable Knowledge Discovery has attracted much interest lately. It is almost a new paradigm shift toward mining more usable and more applicable Knowledge in each specific domain. An action is a new tool in this research area that suggests some changes to the user to gain a profit in his/her domain. Currently, most of action mining methods rely on simple data which describes each object independently. Since social data has more complex structure due to the relationships between individuals, a major problem is that such structural information is not taken into account in the action mining process. This leads to miss some useful Knowledge and profitable actions. Consequently, more effective methods are needed for mining actions. The main focus of this work is to extract cost-effective actions from social networks in which nodes have attributes. The actions suggest optimal changes in nodes’ attributes that are likely to result in changing labels of users to more desired one when they are applied. We develop an action mining method based on Random Walks that naturally combines the information from the network structure with nodes attributes. We formulate action mining as an optimization problem where the goal is to learn a function that varies the values of nodes’ attributes which in turn affect edges’ weights in the network so that the labels of intended individuals are likely to take the desired label while minimizing the cost of incurring the changes. Experiments confirm that the proposed approach outperforms the current state-of-the-art in action mining.
