The Experts below are selected from a list of 7227 Experts worldwide ranked by ideXlab platform

Ananth Grama - One of the best experts on this subject based on the ideXlab platform.

  • Compression, clustering, and pattern discovery in very high-dimensional Discrete-Attribute data sets
    IEEE Transactions on Knowledge and Data Engineering, 2005
    Co-Authors: Mehmet Koyutürk, Ananth Grama, Naren Ramakrishnan
    Abstract:

    This paper presents an efficient framework for error-bounded compression of high-dimensional Discrete-Attribute data sets. Such data sets, which frequently arise in a wide variety of applications, pose some of the most significant challenges in data analysis. Subsampling and compression are two key technologies for analyzing these data sets. The proposed framework, PROXIMUS, provides a technique for reducing large data sets into a much smaller set of representative patterns, on which traditional (expensive) analysis algorithms can be applied with minimal loss of accuracy. We show desirable properties of PROXIMUS in terms of runtime, scalability to large data sets, and performance in terms of capability to represent data in a compact form and discovery and interpretation of interesting patterns. We also demonstrate sample applications of PROXIMUS in association rule mining and semantic classification of term-document matrices. Our experimental results on real data sets show that use of the compressed data for association rule mining provides excellent precision and recall values (above 90 percent) across a range of problem parameters while reducing the time required for analysis drastically. We also show excellent interpretability of the patterns discovered by PROXIMUS in the context of clustering and classification of terms and documents. In doing so, we establish PROXIMUS as a tool for both preprocessing data before applying computationally expensive algorithms and directly extracting correlated patterns.

  • conquest a distributed tool for constructing summaries of high dimensional Discrete Attribute data sets
    SIAM International Conference on Data Mining, 2004
    Co-Authors: Jie Chi, Mehmet Koyutürk, Ananth Grama
    Abstract:

    The problem of constructing bounded-error summaries of binary Attributed data of very high dimensions is an important and difficult one. These summaries enable more expensive analysis techniques to be applied efficiently with little loss in accuracy. Recent work in this area has resulted in the use of Discrete linear algebraic transforms to construct such summaries efficiently. This paper addresses the problem of constructing summaries of distributed datasets. Specifically, the problem can be stated as follows: given a set of n Discrete Attributed vectors distributed across p sites, construct a summary of k << n vectors such that each of the input vectors is within given bounded distance from some output vector. In addition to being algorithmically efficient (i.e., must do no more work than corresponding serial algorithm), the distributed formulation must have low parallelization overheads. We present here, CONQUEST, a tool that achieves excellent performance and scalability for summarizing distributed datasets. In contrast to traditional parallel techniques that distribute the kernel operations, CONQUEST uses a less aggressive parallel formulation that relies on the principle of sampling to reduce communication overhead while maintaining high accuracy. Specifically, each individual site computes its local patterns independently. Various sites cooperate within dynamically orchestrated workgroups to construct consensus patters from these local patterns. Individual sites then decide to participate in the consensus or leave the group. Experimental results on a set of Intel Xeon servers demonstrate that this strategy is capable of excellent performance in terms of compression time, ratio, and accuracy with respect to postprocessing tasks. The communication overhead associated with CONQUEST is also shown to be minimal, making it ideally suited to wide-area deployment. keywords: distributed data mining, compressing binary Attributed vectors, non-orthogonal matrix decompositions, correlations in high dimensions.

  • SDM - Conquest: A Distributed Tool for Constructing Summaries of High-Dimensional Discrete Attribute Data Sets.
    2004
    Co-Authors: Jie Chi, Mehmet Koyutürk, Ananth Grama
    Abstract:

    The problem of constructing bounded-error summaries of binary Attributed data of very high dimensions is an important and difficult one. These summaries enable more expensive analysis techniques to be applied efficiently with little loss in accuracy. Recent work in this area has resulted in the use of Discrete linear algebraic transforms to construct such summaries efficiently. This paper addresses the problem of constructing summaries of distributed datasets. Specifically, the problem can be stated as follows: given a set of n Discrete Attributed vectors distributed across p sites, construct a summary of k

Siu Cheung Hui - One of the best experts on this subject based on the ideXlab platform.

  • CIKM - Multi-Task Neural Network for Non-Discrete Attribute Prediction in Knowledge Graphs
    Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
    Co-Authors: Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui
    Abstract:

    Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-Discrete Attributes for each entity. Intuitively, these Attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-Discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-Discrete Attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for Attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and Attributes that encode information about both tasks. Moreover, such Attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and Attribute value prediction.

  • Multi-task Neural Network for Non-Discrete Attribute Prediction in Knowledge Graphs
    arXiv: Artificial Intelligence, 2017
    Co-Authors: Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui
    Abstract:

    Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-Discrete Attributes for each entity. Intuitively, these Attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-Discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-Discrete Attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for Attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and Attributes that encode information about both tasks. Moreover, such Attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and Attribute value prediction.

Mehmet Koyutürk - One of the best experts on this subject based on the ideXlab platform.

  • Compression, clustering, and pattern discovery in very high-dimensional Discrete-Attribute data sets
    IEEE Transactions on Knowledge and Data Engineering, 2005
    Co-Authors: Mehmet Koyutürk, Ananth Grama, Naren Ramakrishnan
    Abstract:

    This paper presents an efficient framework for error-bounded compression of high-dimensional Discrete-Attribute data sets. Such data sets, which frequently arise in a wide variety of applications, pose some of the most significant challenges in data analysis. Subsampling and compression are two key technologies for analyzing these data sets. The proposed framework, PROXIMUS, provides a technique for reducing large data sets into a much smaller set of representative patterns, on which traditional (expensive) analysis algorithms can be applied with minimal loss of accuracy. We show desirable properties of PROXIMUS in terms of runtime, scalability to large data sets, and performance in terms of capability to represent data in a compact form and discovery and interpretation of interesting patterns. We also demonstrate sample applications of PROXIMUS in association rule mining and semantic classification of term-document matrices. Our experimental results on real data sets show that use of the compressed data for association rule mining provides excellent precision and recall values (above 90 percent) across a range of problem parameters while reducing the time required for analysis drastically. We also show excellent interpretability of the patterns discovered by PROXIMUS in the context of clustering and classification of terms and documents. In doing so, we establish PROXIMUS as a tool for both preprocessing data before applying computationally expensive algorithms and directly extracting correlated patterns.

  • conquest a distributed tool for constructing summaries of high dimensional Discrete Attribute data sets
    SIAM International Conference on Data Mining, 2004
    Co-Authors: Jie Chi, Mehmet Koyutürk, Ananth Grama
    Abstract:

    The problem of constructing bounded-error summaries of binary Attributed data of very high dimensions is an important and difficult one. These summaries enable more expensive analysis techniques to be applied efficiently with little loss in accuracy. Recent work in this area has resulted in the use of Discrete linear algebraic transforms to construct such summaries efficiently. This paper addresses the problem of constructing summaries of distributed datasets. Specifically, the problem can be stated as follows: given a set of n Discrete Attributed vectors distributed across p sites, construct a summary of k << n vectors such that each of the input vectors is within given bounded distance from some output vector. In addition to being algorithmically efficient (i.e., must do no more work than corresponding serial algorithm), the distributed formulation must have low parallelization overheads. We present here, CONQUEST, a tool that achieves excellent performance and scalability for summarizing distributed datasets. In contrast to traditional parallel techniques that distribute the kernel operations, CONQUEST uses a less aggressive parallel formulation that relies on the principle of sampling to reduce communication overhead while maintaining high accuracy. Specifically, each individual site computes its local patterns independently. Various sites cooperate within dynamically orchestrated workgroups to construct consensus patters from these local patterns. Individual sites then decide to participate in the consensus or leave the group. Experimental results on a set of Intel Xeon servers demonstrate that this strategy is capable of excellent performance in terms of compression time, ratio, and accuracy with respect to postprocessing tasks. The communication overhead associated with CONQUEST is also shown to be minimal, making it ideally suited to wide-area deployment. keywords: distributed data mining, compressing binary Attributed vectors, non-orthogonal matrix decompositions, correlations in high dimensions.

  • SDM - Conquest: A Distributed Tool for Constructing Summaries of High-Dimensional Discrete Attribute Data Sets.
    2004
    Co-Authors: Jie Chi, Mehmet Koyutürk, Ananth Grama
    Abstract:

    The problem of constructing bounded-error summaries of binary Attributed data of very high dimensions is an important and difficult one. These summaries enable more expensive analysis techniques to be applied efficiently with little loss in accuracy. Recent work in this area has resulted in the use of Discrete linear algebraic transforms to construct such summaries efficiently. This paper addresses the problem of constructing summaries of distributed datasets. Specifically, the problem can be stated as follows: given a set of n Discrete Attributed vectors distributed across p sites, construct a summary of k

Jie Chi - One of the best experts on this subject based on the ideXlab platform.

  • conquest a distributed tool for constructing summaries of high dimensional Discrete Attribute data sets
    SIAM International Conference on Data Mining, 2004
    Co-Authors: Jie Chi, Mehmet Koyutürk, Ananth Grama
    Abstract:

    The problem of constructing bounded-error summaries of binary Attributed data of very high dimensions is an important and difficult one. These summaries enable more expensive analysis techniques to be applied efficiently with little loss in accuracy. Recent work in this area has resulted in the use of Discrete linear algebraic transforms to construct such summaries efficiently. This paper addresses the problem of constructing summaries of distributed datasets. Specifically, the problem can be stated as follows: given a set of n Discrete Attributed vectors distributed across p sites, construct a summary of k << n vectors such that each of the input vectors is within given bounded distance from some output vector. In addition to being algorithmically efficient (i.e., must do no more work than corresponding serial algorithm), the distributed formulation must have low parallelization overheads. We present here, CONQUEST, a tool that achieves excellent performance and scalability for summarizing distributed datasets. In contrast to traditional parallel techniques that distribute the kernel operations, CONQUEST uses a less aggressive parallel formulation that relies on the principle of sampling to reduce communication overhead while maintaining high accuracy. Specifically, each individual site computes its local patterns independently. Various sites cooperate within dynamically orchestrated workgroups to construct consensus patters from these local patterns. Individual sites then decide to participate in the consensus or leave the group. Experimental results on a set of Intel Xeon servers demonstrate that this strategy is capable of excellent performance in terms of compression time, ratio, and accuracy with respect to postprocessing tasks. The communication overhead associated with CONQUEST is also shown to be minimal, making it ideally suited to wide-area deployment. keywords: distributed data mining, compressing binary Attributed vectors, non-orthogonal matrix decompositions, correlations in high dimensions.

  • SDM - Conquest: A Distributed Tool for Constructing Summaries of High-Dimensional Discrete Attribute Data Sets.
    2004
    Co-Authors: Jie Chi, Mehmet Koyutürk, Ananth Grama
    Abstract:

    The problem of constructing bounded-error summaries of binary Attributed data of very high dimensions is an important and difficult one. These summaries enable more expensive analysis techniques to be applied efficiently with little loss in accuracy. Recent work in this area has resulted in the use of Discrete linear algebraic transforms to construct such summaries efficiently. This paper addresses the problem of constructing summaries of distributed datasets. Specifically, the problem can be stated as follows: given a set of n Discrete Attributed vectors distributed across p sites, construct a summary of k

Yi Tay - One of the best experts on this subject based on the ideXlab platform.

  • CIKM - Multi-Task Neural Network for Non-Discrete Attribute Prediction in Knowledge Graphs
    Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
    Co-Authors: Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui
    Abstract:

    Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-Discrete Attributes for each entity. Intuitively, these Attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-Discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-Discrete Attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for Attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and Attributes that encode information about both tasks. Moreover, such Attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and Attribute value prediction.

  • Multi-task Neural Network for Non-Discrete Attribute Prediction in Knowledge Graphs
    arXiv: Artificial Intelligence, 2017
    Co-Authors: Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui
    Abstract:

    Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-Discrete Attributes for each entity. Intuitively, these Attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-Discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-Discrete Attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for Attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and Attributes that encode information about both tasks. Moreover, such Attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and Attribute value prediction.