Handling Missing Data in Extended Possibility-based Fuzzy Relational Databases

Liu, Julie Yu‐Chih; Huang, Chiung-Hua

doi:10.1109/ibica.2012.39

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…The problem of grouping data with missing values was rectified. The experiments conducted have shown the efficiency of the proposed algorithm in imputing missing values so that better clustering results could be obtained than the existing methods taken for comparative analysis.Julie Yu-Chih Liu et al [10] proposed a novel missing data approach to rectify the problem of missing values in the extended possibility-based fuzzy relational (EPFR) databases. Missing data imputation was proposed using fuzzy functional dependencies and their inference rules.…”

Section: Related Workmentioning

confidence: 99%

Imputing the Missing Values in IoT using FRBIM

Mary¹

2019

IJRTE

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The Internet of Things (IoT) is the new-fangled communication paradigm in which the internet is stretched out from the virtual world to intermingle with the objects in the physical world. It unleashes a new dimension of services but at the same time, colossal challenges have to be conquered to reap the full benefits of the IoT. One such challenge is missing data imputation in Internet of Things. The presence of missing values hampers the subsequent processes such as prediction, control, decision making etc. due to the dependency of these processes on complete information. In this paper, a novel FRBIM (Fuzzy Rule-Based Imputation Model) model is proposed to impute missing data based on the characteristics of IoT data to accomplish high accuracy rate. Experimental results have proved that the proposed method has outperformed the existing KNN and AKE imputation model in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Imputing the Missing Values in IoT using FRBIM

Mary¹

2019

IJRTE

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“…3. and work that does not deal with simple values (fuzzy databases, where values can be fuzzy functions) [25,26,13,23,5].…”

Section: Related Workmentioning

confidence: 99%

On Desirable Semantics of Functional Dependencies over Databases with Incomplete Information

Badia

Lemire

2018

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Codd's relational model describes just one possible world. To better cope with incomplete information, extended database models allow several possible worlds. Vague tables are one such convenient extended model where attributes accept sets of possible values (e.g., the manager is either Jill or Bob). However, conceptual database design in such cases remains an open problem. In particular, there is no canonical definition of functional dependencies (FDs) over possible worlds (e.g., each employee has just one manager). We identify several desirable properties that the semantics of such FDs should meet including Armstrong's axioms, the independence from irrelevant attributes, seamless satisfaction and implied by strong satisfaction. We show that we can define FDs such that they have all our desirable properties over vague tables. However, we also show that no notion of FD can satisfy all our desirable properties over a more general model (disjunctive tables). Our work formalizes a trade-off between having a general model and having well-behaved FDs.

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Handling Missing Data in Extended Possibility-based Fuzzy Relational Databases

Cited by 2 publications

References 38 publications

Imputing the Missing Values in IoT using FRBIM

Imputing the Missing Values in IoT using FRBIM

On Desirable Semantics of Functional Dependencies over Databases with Incomplete Information

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