Unsupervised Classification of Mixed Data Type of Attributes Using Genetic Algorithm (Numeric, Categorical, Ordinal, Binary, Ratio-Scaled)

Rastogi, Rohit; Agarwal, Saumya; Sharma, Palak; Kaul, Uarvarshi; Jain, Shilpi

doi:10.1007/978-81-322-1771-8_11

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…For example, timetabling is inherently a dependent problem as a change in one timeslot inevitably causes changes to some or all of the other timeslots [41]. In such situations clustering of the data [42] is useful to create independent groups of points. A survey can be found in [43].…”

Section: Evolutionary Algorithms and Hybrid Techniquesmentioning

confidence: 99%

Bio-inspired computing tools and applications: position paper

Gedeon

2017

Int. j. inf. tecnol.

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The confluence of significant computational power and inexpensive sensors provides the opportunity to reliably collect large volumes of information from the world and extract humanly useful information resources. This paper reviews a coherent body of work over the last 20? years focused on development of advanced bio-inspired computing techniques, and their applications primarily for human related data in behaviour and human centered computing. We close with a synthesis proposing an experiment analysis methodology combining these tools.

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Section: Evolutionary Algorithms and Hybrid Techniquesmentioning

confidence: 99%

Bio-inspired computing tools and applications: position paper

Gedeon

2017

Int. j. inf. tecnol.

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“…In the majority of cases, fuzzy clustering algorithms have been verified to be a better method than hard clustering in dealing with discrimination of similar structures [1], dataset in dimensional spaces [2], and is more useful for unlabeled data with outliers [3]. Fuzzy C-means proved to offer better solutions in machine learning, and image processing than hard clustering such as Ward's clustering and the k mean algorithm [4][5][6][7][8][9]. Generally, fuzzy c-mean has 66% accuracy while Gustafson-Kessel scored 70% [10].…”

Section: Introductionmentioning

confidence: 99%

Data Clustering for Fuzzyfier Value Derivation

Cho¹

2022

Fuzzy Systems - Theory and Applications

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The fuzzifier value m is improving significant factor for achieving the accuracy of data. Therefore, in this chapter, various clustering method is introduced with the definition of important values for clustering. To adaptively calculate the appropriate purge value of the gap type −2 fuzzy c-means, two fuzzy values m1 and m2 are provided by extracting information from individual data points using a histogram scheme. Most of the clustering in this chapter automatically obtains determination of m1 and m2 values that depended on existent repeated experiments. Also, in order to increase efficiency on deriving valid fuzzifier value, we introduce the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), as one of advanced fuzzy clustering method to classify a fixed pattern. In Efficient IT2PFCM method, proper fuzzifier values for each data is obtained from an algorithm including histogram analysis and Gaussian Curve Fitting method. Using the extracted information form fuzzifier values, two modified fuzzifier value m1 and m2 are determined. These updated fuzzifier values are used to calculated the new membership values. Determining these updated values improve not only the clustering accuracy rate of the measured sensor data, but also can be used without additional procedure such as data labeling. It is also efficient at monitoring numerous sensors, managing and verifying sensor data obtained in real time such as smart cities.

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