Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Zhou, Kaile; Fu, Chao; Yang, Shanlin

doi:10.1007/s11432-014-5146-0

Cited by 49 publications

(34 citation statements)

References 41 publications

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“…We ran the FA for different values of m in order to find the optimal parameter of fuzziness and chose the one that gives the best results. Recently, Zhou et al reported that the optimal interval of the parameter m is from 2 to 3.5. In our study, we range the parameter m from 1.5 to 3.5 with increments of 0.5. Shape of membership functions: Many shapes of membership functions can be used such as triangular, trapezoidal, and Gaussian. Number of fuzzy clusters: for a real dataset, the number of clusters, k , is usually unknown in advance.…”

Section: Empirical Designmentioning

confidence: 99%

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Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Idri

Abnane

Abran

2017

J Software Evolu Process

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Software development effort estimation (SDEE) plays a primary role in software project management. But choosing the appropriate SDEE technique remains elusive for many project managers and researchers. Moreover, the choice of a reliable estimation accuracy measure is crucial because SDEE techniques behave differently given different accuracy measures. The most widely used accuracy measures in SDEE are those based on magnitude of relative error (MRE) such as mean/median MRE (MMRE/MedMRE) and prediction at level p (Pred(p)), which counts the number of observations where an SDEE technique gave MREs lower than p. However, MRE has proven to be an unreliable accuracy measure, favoring SDEE techniques that underestimate. Consequently, an unbiased measure called standardized accuracy (SA) has been proposed. This paper deals with the Pred(p) and SA measures. We investigate (1) the consistency of Pred(p) and SA as accuracy measures and SDEE technique selectors, and (2) the relationship between Pred(p) and SA. The results suggest that Pred(p) is less biased towards underestimates and generally selects the same best technique as SA. Moreover, SA and Pred(p) measure different aspects of technique performance, and SA may be used as a predictor of Pred(p) by means of the 3 association rules.

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Section: Empirical Designmentioning

confidence: 99%

“…We ran the FA for different values of m in order to find the optimal parameter of fuzziness and chose the one that gives the best results. Recently, Zhou et al 45 reported that the optimal interval of the parameter m is from 2 to 3.5. In our study, we range the parameter m from 1.5 to 3.5 with increments of 0.5.…”

Section: Case Adaptationmentioning

confidence: 99%

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Idri

Abnane

Abran

2017

J Software Evolu Process

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“…To discovery valuable knowledge and fully realize the business potential of energy big data, various big data analytics techniques, such as data quality evaluation and modeling [103][104][105], data clustering and classification [68,[106][107][108][109], stream data processing [110][111][112], knowledge inference [113,114], statistical machine learning [115], neural networks modeling and deep learning [116,117], can be implemented on the data. The objective of energy big data analytics is to develop more effective and efficient data-driven applications and services.…”

Section: Energy Big Data Driven Applications In Energy Internetmentioning

confidence: 99%

Energy Internet: The business perspective

2016

Self Cite

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“…However, there is no recommended value of m . Zhou et al reported that the optimal interval of the m parameter is from 2 to 3.5. In this study, we ranged m from 1.5 to 3.5 in increments of 0.1. Number of fuzzy clusters: The number of clusters was varied within the interval for all features in each dataset.…”

Section: Empirical Designmentioning

confidence: 99%

“…• Parameter of fuzziness (m): Prior studies on FCM reported that the result of clustering is significantly affected by the variation of parameter m. 86,87 However, there is no recommended value of m. Zhou et al 88 reported that the optimal interval of the m parameter is from 2 to 3.5.…”

Section: Configuration Of Filters and Single Fuzzy Analogy Techniquesmentioning

confidence: 99%

Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

Hosni

Idri

Abran

2018

J Software Evolu Process

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Researchers have developed and evaluated many techniques to deliver accurate estimates of the effort required to complete a new software program. Among these, analogy has emerged as a very promising technique, in particular the fuzzy analogy estimation technique that uses the fuzzy logic concepts in order to deal with both categorical and numerical data. The aim of this paper is twofold: (1) evaluate the impact of 3 filters on the predictive ability of single and ensemble fuzzy analogy techniques and (2) assess whether filters could be a source of diversity for fuzzy analogy ensembles. Moreover, it compares the filter single and ensemble fuzzy analogy techniques with fuzzy analogy ensembles built without using feature selection over 6 datasets. The overall results suggest that (1) more accurate estimates are generated when filters were used with single and ensemble fuzzy analogy techniques, (2) filter single fuzzy analogy techniques outperformed filter fuzzy analogy ensembles, and (3) fuzzy analogy ensembles without feature selection were more accurate than filter single and ensemble techniques. Therefore, though the use of feature selection techniques led single and ensemble fuzzy analogy to generate accurate estimations, they failed to be a source of diversity for fuzzy analogy ensembles. Hence, constructing fuzzy analogy homogenous ensembles that combine single fuzzy analogy techniques with different parameter configurations still generate better accuracy than filter fuzzy analogy ensembles. However, further empirical evaluations of filter/wrappers fuzzy analogy ensembles are required in order to confirm or refute these findings.

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Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

Cited by 49 publications

References 41 publications

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Evaluating Pred(p) and standardized accuracy criteria in software development effort estimation

Energy Internet: The business perspective

Evaluating filter fuzzy analogy homogenous ensembles for software development effort estimation

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