Relational Data

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Piotr Hońko - One of the best experts on this subject based on the ideXlab platform.

  • Recent granular computing frameworks for mining Relational Data
    Artificial Intelligence Review, 2018
    Co-Authors: Piotr Hońko
    Abstract:

    A lot of Data currently being collected is stored in Databases with a Relational structure. The process of knowledge discovery from such Data is a more challenging task compared with single table Data. Granular computing, which has successfully been applied to mining Data storable in single tables, is a promising direction for discovering knowledge from Relational Data. This paper summarizes some recent developments in the area of application of granular computing to mining Relational Data. Four granular computing frameworks for processing Relational Data are introduced and compared. The paper shows how each of the frameworks represents Relational Data, constructs information granules and build patterns based on the granules. A generic system that can employ any of the frameworks to discover knowledge from Relational Data is also outlined.

  • Rough-Granular Computing for Relational Data
    Thriving Rough Sets, 2017
    Co-Authors: Piotr Hońko
    Abstract:

    Rough set theory and granular computing have widely been applied in Data mining. They have been used separately as well as a combined approach called rough-granular computing. The usefulness of this approach in Data mining is the driving force for employing it to improve processing of Relational Data. This chapter introduces three rough-granular approaches dedicated to handle complex Data such as Relational one. Each of them processes Relational Data as granules and use the tolerance rough set model to deal with possible uncertainty in Data. The chapter also compares the three approaches in terms of construction of information system, information granules, and approximation spaces.

  • Information System for Relational Data
    Studies in Computational Intelligence, 2017
    Co-Authors: Piotr Hońko
    Abstract:

    This chapter develops a general granular computing based framework for mining Relational Data. The framework provides a granular representation of Relational Data that is constructed based on target objects and the sets of background objects related to the target ones. The chapter also shows the possibility of deriving Relational patterns from the granular representation.

  • Properties of Granular-Relational Data Mining Framework
    Studies in Computational Intelligence, 2017
    Co-Authors: Piotr Hońko
    Abstract:

    This chapter investigates properties of the general granular computing based framework for mining Relational Data. The properties enable to define the generality relation on the set of alternative granular representations of Relational Data. This chapter also discusses the usefulness of the properties in tasks such as Relational objects representation, search space limitation, and Relational patterns generation.

  • Properties of a Granular Computing Framework for Mining Relational Data
    International Journal of Intelligent Systems, 2016
    Co-Authors: Piotr Hońko
    Abstract:

    This work investigates properties of a framework for mining Relational Data. The framework is constructed based on granular computing theory and is equipped with a method for deriving information granules from Relational Data. Such granules are the basis for discovering knowledge of a different type. It is shown in the paper that thanks to the properties one can improve the performance of tasks such as Relational objects representation, search space limitation, and Relational patterns generation.

Luc Raedt - One of the best experts on this subject based on the ideXlab platform.

  • Relational Data factorization
    Machine Learning, 2017
    Co-Authors: Sergey Paramonov, Matthijs Leeuwen, Luc Raedt
    Abstract:

    Motivated by an analogy with matrix factorization, we introduce the problem of factorizing Relational Data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In Relational Data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. Thus, Relational Data factorization is a Relational analog of matrix factorization; it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. The result of Relational Data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. Therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original Data). Relational Data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving Relational Data factorization problems.

  • Machine Learning - Relational Data factorization
    Machine Learning, 2017
    Co-Authors: Sergey Paramonov, Matthijs Van Leeuwen, Luc Raedt
    Abstract:

    Motivated by an analogy with matrix factorization, we introduce the problem of factorizing Relational Data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In Relational Data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. Thus, Relational Data factorization is a Relational analog of matrix factorization; it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. The result of Relational Data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. Therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original Data). Relational Data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving Relational Data factorization problems.

S. Sen - One of the best experts on this subject based on the ideXlab platform.

  • ICWET - Probabilistic Relational Data mining
    Proceedings of the International Conference and Workshop on Emerging Trends in Technology, 2010
    Co-Authors: S. Sen
    Abstract:

    Probabilistic Relational Data Mining (PRDM) refers to the use of Knowledge discovery in Database (KDD) methods of learning probabilistic statistical models from Relational Data that has information about several types of objects. This is usually the case when the Database has more than one table. PRDM provides techniques for discovering descriptive models, including relationships, correlations and causal dependencies, embedded in a set of objects as well as their component attributes. In essence, it is a marriage of probabilistic modeling, multi-Relational Database modeling, and object oriented modeling. The three modeling processes are integrated together into a Data mining system to fulfill the overall modeling task, in which, intuitively speaking, Database modeling plays a role of input, probabilistic modeling is like an output, and object-oriented modeling provides necessary background information.

  • Robust fuzzy clustering of Relational Data
    IEEE Transactions on Fuzzy Systems, 2002
    Co-Authors: Rajesh N. Dave, S. Sen
    Abstract:

    Popular Relational-Data clustering algorithms, Relational dual of fuzzy c-means (RFCM), non-Euclidean RFCM (NERFCM) (both by Hathaway et al), and FANNY (by Kaufman and Rousseeuw) are examined. A new algorithm, which is a generalization of FANNY, called the fuzzy Relational Data clustering (FRC) algorithm, is introduced, having an identical objective functional as RFCM. However, the FRC does not have the restriction of RFCM, which is that the Relational Data is derived from Euclidean distance as the measure of dissimilarity between the objects, and it also does not have limitations of FANNY, including the use of a fixed membership exponent, or a fuzzifier exponent, m. The FRC algorithm is further improved by incorporating the concept of Dave's object Data noise clustering (NC) algorithm, done by proposing a concept of noise-dissimilarity. Next, based on the constrained minimization, which includes an inequality constraint for the memberships and corresponding Kuhn-Tucker conditions, a noise resistant, FRC algorithm is derived which works well for all types of non-Euclidean dissimilarity Data. Thus it is shown that the extra computations for Data expansion (/spl beta/-spread transformation) required by the NERFCM algorithm are not necessary. This new algorithm is called robust non-Euclidean fuzzy Relational Data clustering (robust-NE-FRC), and its robustness is demonstrated through several numerical examples. Advantages of this new algorithm are: faster convergence, robustness against outliers, and ability to handle all kinds of Relational Data, including non-Euclidean. The paper also presents a new and better interpretation of the noise-class.

  • Clustering of Relational Data containing noise and outliers
    1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), 1
    Co-Authors: S. Sen, Rajesh N. Dave
    Abstract:

    The concept of noise clustering algorithm is applied to several fuzzy Relational Data clustering algorithms to make them more robust against noise and outliers. The methods considered include techniques proposed by Roubens (1978), Hathaway et al. (1994) and FANNY by Kaufman and Rouseeuw (1990). A new fuzzy Relational Data clustering (FRC) algorithm is proposed through generalization of FANNY. The FRC algorithm is shown to have the same objective functional as the Relational fuzzy c-means algorithm. However, through use of direct objective function minimization based on the Lagrangian multiplier technique, the necessary conditions for minimization are derived without imposition of the restriction that the Relational Data is derived from Euclidean measure of distance from object Data. Robustness of the new algorithm is demonstrated through several examples.

Mario Boley - One of the best experts on this subject based on the ideXlab platform.

  • Interesting pattern mining in multi-Relational Data
    Data Mining and Knowledge Discovery, 2014
    Co-Authors: Eirini Spyropoulou, Tijl Bie, Mario Boley
    Abstract:

    Mining patterns from multi-Relational Data is a problem attracting increasing interest within the Data mining community. Traditional Data mining approaches are typically developed for single-table Databases, and are not directly applicable to multi-Relational Data. Nevertheless, multi-Relational Data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-Relational Data. We propose a new syntax for multi-Relational patterns as complete connected subsets of Database entities. We show how this pattern syntax is generally applicable to multi-Relational Data, while it reduces to well-known tiles " Geerts et al. (Proceedings of Discovery Science, pp 278-289, 2004 )" when the Data is a simple binary or attribute-value table. We propose RMiner, a simple yet practically efficient divide and conquer algorithm to mine such patterns which is an instantiation of an algorithmic framework for efficiently enumerating all fixed points of a suitable closure operator "Boley et al. (Theor Comput Sci 411(3):691-700, 2010 )". We show how the interestingness of patterns of the proposed syntax can conveniently be quantified using a general framework for quantifying subjective interestingness of patterns "De Bie (Data Min Knowl Discov 23(3):407-446, 2011b )". Finally, we illustrate the usefulness and the general applicability of our approach by discussing results on real-world and synthetic Databases.[PUBLICATION ABSTRACT]

Lihui Chen - One of the best experts on this subject based on the ideXlab platform.

  • A Fuzzy Approach for Multitype Relational Data Clustering
    IEEE Transactions on Fuzzy Systems, 2012
    Co-Authors: Jian-ping Mei, Lihui Chen
    Abstract:

    Mining interrelated Data among multiple types of objects or entities is important in many real-world applications. Despite extensive study on fuzzy clustering of vector space Data, very limited exploration has been made on fuzzy clustering of Relational Data that involve several object types. In this paper, we propose a new fuzzy clustering approach for multitype Relational Data (FC-MR). In FC-MR, different types of objects are clustered simultaneously. An object is assigned a large membership with respect to a cluster if its related objects in this cluster have high rankings. In each cluster, an object tends to have a high ranking if its related objects have large memberships in this cluster. The FC-MR approach is formulated to deal with multitype Relational Data with various structures. The objective function of FC-MR is locally optimized by an efficient iterative algorithm, which updates the fuzzy membership matrix and the ranking matrix of one type at once while keeping those of other types constant. We also discuss the simplified FC-MR for multitype Relational Data with two special structures, namely, star-structure and extended star-structure. Experimental studies are conducted on benchmark document Datasets to illustrate how the proposed approach can be applied flexibly under different scenarios in real-world applications. The experimental results demonstrate the feasibility and effectiveness of the new approach compared with existing ones.

  • A Fuzzy Approach for Multi-Type Relational Data Clustering
    2011
    Co-Authors: Jian-ping Mei, Lihui Chen
    Abstract:

    Mining interrelated Data among multiple types of objects or entities is important in many realworld applications. Despite of extensive study on fuzzy clustering of vector space Data, very limited exploration has been made on fuzzy clustering of Relational Data involving several object types. In this paper, we propose a new fuzzy approach for clustering multi-type Relational Data (FC-MR), which simultaneously clusters different types of objects. In FC-MR, an object is assigned with a large membership to a cluster if its related objects in this cluster have high rankings. In each cluster, an object tends to have a high ranking if its related objects have large memberships in this cluster. The FC-MR approach is formulated to deal with multi-type Relational Data with various structures. The objective function of FC-MR is locally optimized by an efficient iterative algorithm which updates the fuzzy membership matrix and the ranking matrix of one type at once while keeping those of other types constant. We also discuss the simplified FC-MR for multi-type Relational Data with two special structures namely star-structure and extended star-structure. Experimental studies are conducted on benchmark document Datasets to illustrate how the proposed approach can be applied flexibly under different scenarios in real-world applications. The experimental results demonstrate the feasibility and effectiveness of the new approach compared with existing ones.