Some studies on fuzzy clustering of psychosis data

Chattopadhyay, Subhagata; Pratihar, Dilip Kumar; Sarkar, Sourav

doi:10.1504/ijbidm.2007.013934

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…Uses of IT are so many, ranging from telemedicine [6], image processing [7], data management [8], signal processing [9], and knowledge management [10] to medical expert system design [10]. Data mining and knowledge management, on the other hand, have contributed a lot in understanding the ad-hoc clinical data and extracting hidden knowledge from it.…”

Section: Background and Motivationmentioning

confidence: 99%

A Novel Mathematical Approach to Diagnose Premenstrual Syndrome

Chattopadhyay¹,

Acharya

2011

J Med Syst

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Diagnosis of Premenstrual syndrome (PMS) is a research challenge due to its subjective presentation. An undiagnosed PMS case is often termed as 'borderline' ('B') that further add to the diagnostic fuzziness. This study proposes a methodology to diagnose PMS cases using a combined knowledge engineering and soft computing techniques. According to the guidelines of American College of Gynecology (ACOG), ten symptoms have been selected and technically processed for 50 cases each having class labels-'B' or 'NB' (not borderline) using domain expertise. Any Attribute that fails normality test has been excluded from the study. Decision tree (DT) has then been induced in obtaining the initial class boundaries and mining the important Attributes to classify PMS cases. Prior doing so, the best split criterion has been set using the maximum information gain measure. Initial information about classification boundaries are finally used to measure fuzzy membership values and the corresponding firing strengths have been measured for final classification of PMS 'B' cases.

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Section: Background and Motivationmentioning

confidence: 99%

A Novel Mathematical Approach to Diagnose Premenstrual Syndrome

Chattopadhyay¹,

Acharya

2011

J Med Syst

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“…We use K-means [5], [6]algorithm to cluster the data set. Once clusters are formed, we calculate average value for the dataset.…”

Section: Proposed Outlier Detection Methodsmentioning

confidence: 99%

Pruning based method for outlier detection

Pamula

Deka

Nandi

2012

2012 Third International Conference on Emerging Applications of Information Technology

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In this paper we propose a method to capture outliers. We apply a clustering algorithm to divide the dataset into independent clusters. The clusters which are dense in nature doesnot contain outliers. And the clusters which are sparse are probable candidate clusters for outliers. Pruning the dense clusters makes the dataset small and sparse. For the unpruned points we calculated a distance based outlier score. The computations needed for calculating the outlier score reduces considerably due to the pruning of many points. Based on the outlier score we declare the top-n points with the highest score as outliers. The experimental results using real data set demonstrate that even though the number of computations are less, the proposed method performs better than the existing method.

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“…A comparative study of FCM and Entropy-based Fuzzy Clustering (EFC) algorithm has been conducted by Chattopadhyay et al (2007a) and Chattopadhyay et al (2007b). In the second study, clusters were visualised using Self Organising Map (SOM).…”

Section: Applications Of Fcm and Fknn In Neuropsychiatry: A Brief Reviewmentioning

confidence: 99%

Mathematical modelling of doctors' perceptions in the diagnosis of depression: a novel approach

Chattopadhyay¹

2013

IJBET

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This paper is a novel attempt to model a perception process in the diagnosis of depression. In order to do so, two Fuzzy Clustering Techniques (FCT), such as Fuzzy C-means (FCM) and Fuzzy k-Nearest Neighbour (FkNN) are applied. Both the techniques have a special parameter called as 'cluster fuzzifier' (m), which determines the degree of fuzziness between any two clusters. Hence, by varying 'm', one can manipulate the partition between the clusters. Thus, appropriate tuning of 'm' is critical to obtain the desired number of good quality clusters. The paper proposes that 'm' mathematically mimics doctors' diagnostic perceptions, which needs to be tuned appropriately for making a correct diagnosis, i.e. assigning appropriate class labels of depression. Having proposed this, the paper examines how 'm' influences the clustering task, on a sample of real-world depression cases.

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Some studies on fuzzy clustering of psychosis data

Cited by 16 publications

References 19 publications

A Novel Mathematical Approach to Diagnose Premenstrual Syndrome

A Novel Mathematical Approach to Diagnose Premenstrual Syndrome

Pruning based method for outlier detection

Mathematical modelling of doctors' perceptions in the diagnosis of depression: a novel approach

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