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Spatial analysis for interval-valued data. [PDF]
J Appl Stat, 2023 Symbolic data analysis deals with complex data with symbolic objects, such as lists, histograms, and intervals. Spatial analysis for symbolic data is relatively underexplored. To fill the gap, this paper proposes a statistical framework for spatial interval-valued data (SIVD) analysis.
Workman A, Song JJ.
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Descriptive statistics for symbolic interval-valued data
The Indian Journal of Agricultural Sciences, 2014 It is, by now, well recognized that real data are in intervals and not in points. Unfortunately, classical statistical theory is not capable of handling data in intervals. Here, methodology for drawing univariate and bivariate histograms and computation
H K RANGANATH, PRAJNESHU, HIMADRI GHOSH
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Matrix Factorization with Interval-Valued Data [PDF]
2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020 With many applications relying on multi-dimensional datasets for decision making, matrix factorization (or decomposition) is becoming the basis for many knowledge discoveries and machine learning tasks, from clustering, trend detection, anomaly detection, to correlation analysis. Unfortunately, a major shortcoming of matrix analysis operations is that,
Mao-Lin Li +3 more
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Intuitionistic and Interval-Valued Fuzzy Set Representations for Data Mining
Algorithms, 2022 Data mining refers to a variety of techniques in the fields of databases, machine learning and pattern recognition. The intent is to obtain useful patterns and associations from a large collection of data.
Fred Petry, Ronald Yager
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Selection of Transportation Companies and Their Mode of Transportation for Interval Valued Data [PDF]
Neutrosophic Sets and Systems, 2017 The paper presents selection of transportation companies and their mode of transportation for interval valued neutrosophic data. The paper focuses on the application of distance measures to select mode of transportation for transportation companies.
Dalbinder Kour, Kajla Basu
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Interval valued multi criteria decision making methods for the selection of flexible manufacturing system [PDF]
International Journal of Data and Network Science, 2019 In real world multi criteria decision making (MCDM) problem, it is tough to solve a decision matrix with vague and imprecise data. The degree of impreciseness depends on the kind of data avail-able. For interval valued data this impreciseness is less and
Manoj Mathew, Joji Thomas
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Probabilistic processing of interval-valued sensor data [PDF]
Proceedings of the 5th workshop on Data management for sensor networks, 2008 When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two variants on the Hidden Markov Model in which this is possible: a reading extends over an arbitrary number of hidden states.
Evers, S. +2 more
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IEEE Access, 2022 Developing an effective interval-valued time series (ITS) forecasting scheme for electric power generation is an important issue for energy operators and governments when making energy strategic decisions.
Ting-Jen Chang +3 more
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KNN Data Filling Algorithm for Incomplete Interval-Valued Fuzzy Soft Sets
International Journal of Computational Intelligence Systems, 2023 As a generalization of the fuzzy soft set, interval-valued fuzzy soft set is viewed as a more resilient and powerful tool for dealing with uncertain information.
Xiuqin Ma +3 more
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Similarity measure for aggregated fuzzy numbers from interval-valued data
Soft Computing Letters, 2020 This paper presents a method to compute the degree of similarity between two aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA).
Justin Kane Gunn +2 more
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