Issues on critical objects in mining algorithms

2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI)

Ortiz-Arroyo

Choroś

et al. 2016

Providing a flexible environment to process data objects is a desirable goal of machine learning algorithms. In fuzzy and possibilistic methods, the relevance of data objects is evaluated and a membership degree is assigned. However, some critical objects objects have the potential ability to affect the performance of the clustering algorithms if they remain in a specific cluster or they are moved into another. In this paper we analyze and compare how critical objects affect the behaviour of fuzzy possibilistic methods in several data sets. The comparison is based on the accuracy and ability of learning methods to provide a proper searching space for data objects. The membership functions used by each method when dealing with critical objects is also evaluated. Our results show that relaxing the conditions of participation for data objects in as many partitions as they can, is beneficial.

Section: Properties Of the New Methodsmentioning

confidence: 91%

Section: Thesis Organizationmentioning

confidence: 99%

Section: Example In Geometrymentioning

confidence: 99%

“…14: The critical sample. Information for the critical sample and the other samples in the same (square) cluster 39…”

mentioning

confidence: 99%

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Applying bounded fuzzy possibilistic method on critical objects

2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI)

Ortiz-Arroyo

Choroś

et al. 2016

“…The aim of this paper is to remove restrictions on data objects to participate in as much clusters as they can [24]. Lack of this consideration in learning methods weakens the accuracy and the capability of the proposed methods [25]. The following examples explore the issues on conventional methods in two dimensional search space.…”

Section: Challenges On Conventional Methodsmentioning

confidence: 99%

Bounded Fuzzy Possibilistic Method

2020

Fuzzy Sets and Systems

This paper introduces Bounded Fuzzy Possibilistic Method (BFPM) by addressing several issues that previous clustering/classification methods have not considered. In fuzzy clustering, object's membership values should sum to 1. Hence, any object may obtain full membership in at most one cluster. Possibilistic clustering methods remove this restriction. However, BFPM differs from previous fuzzy and possibilistic clustering approaches by allowing the membership function to take larger values with respect to all clusters. Furthermore, in BFPM, a data object can have full membership in multiple clusters or even in all clusters. BFPM relaxes the boundary conditions (restrictions) in membership assignment. The proposed methodology satisfies the necessity of obtaining full memberships and overcomes the issues with conventional methods on dealing with overlapping. Analysing the objects' movements from their own cluster to another (mutation) is also proposed in this paper. BFPM has been applied in different domains in geometry, set theory, anomaly detection, risk management, diagnosis diseases, and other disciplines. Validity and comparison indexes have been also used to evaluate the accuracy of BFPM. BFPM has been evaluated in terms of accuracy, fuzzification constant (different norms), object's movement analysis, and covering diversity. The promising results prove the importance of considering the proposed methodology in learning methods to track the behaviour of data objects, in addition to obtain accurate results.

Bounded Fuzzy Possibilistic Method Reveals Information about Lung Cancer through Analysis of Metabolomics

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Cheng

Christiani

et al. 2020

Learning methods, such as conventional clustering and classification, have been applied in diagnosing diseases to categorize samples based on their features. Going beyond clustering samples, membership degrees represent to what degree each sample belongs to a cluster. Variation of membership degrees in each cluster provides information about the cluster as a whole and each sample individually which enables to have insights toward precision medicine. Membership degrees are measured more accurately through removing restrictions from clustering samples. Bounded Fuzzy Possibilistic Method (BFPM) introduces a membership function that keeps the search space flexible to cluster samples with higher accuracy. The method evaluates samples for their movement from one cluster to another. This technique allows us to find critical samples in advance those with potential ability to belong to other clusters in the near future. BFPM was applied on metabolomics of individuals in a lung cancer case-control study. Metabolomics as proximal molecular signals to the actual disease processes may serve as strong biomarkers of current disease process. The goal is to know whether serum metabolites of healthy human can be differentiated from those with lung cancer. Using BFPM, some differences were observed, the pathology data were evaluated, and critical samples were recognized.