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1

Handling dimensionality of ambiguity using ensemble classification in social networks to detect multi-label sentiment polarity

Sonawane Sudarshan S., Kolhe Satish R.

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 1

7--26

EN

With ever-increasing demand, social media platforms are rapidly developing to enable users to express and share their opinions on a variety of topics. Twitter is one such social media site. This platform enables a comprehensive view of the social media target setting, which may include products, social events, political scenarios, and administrative resolutions. The accessible tweets expressing the target audience’s perspective are frequently impacted by ambiguity caused by natural language processing (NLP) limitations. By classifying tweets according to their sentiment polarity, we can determine whether they express a good or negative point of view, a neutral opinion, or an input tweet that is irrelevant to the sentiment polarity context. Categorizing tweets according to their sentiment can assist future activities within the target domain in constructively evaluating the sentiment polarity and enabling improved decision-making based on the observed sentiment polarity. In this study, tweets that were previously categorized with one of the sentiment polarities were used to conduct predictive analytics of the new tweet to determine its sentiment polarity. The ambiguity of the tweets corpus utilized in the training phase is a critical limitation of the sentiment categorization procedure. While several recent models proposed sentiment classification algorithms, they confined themselves to two labels: positive and negative opinion, oblivious to the plague of ambiguity in the training corpus. In this regard, a novel multi-label classification of sentiment polarity called handling dimensionality of ambiguity using ensemble classification (HAD-EC) method, which diffuses ambiguity and thus minimizes false alerts, is proposed. The experimental assessment validates the HAD-EC approach by comparing the suggested model’s performance to other two existing models.

2

Granular representation of the information potentialof variables –application example

Kiersztyn Adam, Gandzel Agnieszka, Celiński Maciej, Koczan Leopold

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2021

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T. 11, nr 3

40--44

EN

With the introduction to the science paradigm of Granular Computing, in particular, information granules, the way of thinking about data has changed gradually. Both specialists and scientists stopped focusing on the single data records themselves, but began to look at the analyzed data in a broader context, closer to the way people think. This kind of knowledge representation is expressed, in particular, in approaches based on linguistic modelling or fuzzy techniques such as fuzzy clustering. Therefore, especially important from the point of view of the methodology of data research, is an attempt to understand their potential as information granules. In this study, we will present special cases of using the innovative method of representing the information potential of variables with the use of information granules. In a series of numerical experiments based on both artificially generated data and ecological data on changes in bird arrival dates in the context of climate change, we demonstrate the effectiveness of the proposed approach using classic, not fuzzy measures building information granules.

PL

Wraz z wprowadzeniem do nauki paradygmatu obliczeń ziarnistych, w szczególności ziaren informacji, sposób myślenia o danych stopniowo się zmieniał. Zarówno specjaliści, jak i naukowcy przestali skupiać się na samych rekordach pojedynczych danych, ale zaczęli patrzeć na analizowane dane w szerszym kontekście, bliższym ludzkiemu myśleniu. Ten rodzaj reprezentacji wiedzy wyraża się w szczególności w podejściach opartych na modelowaniu językowym lub technikach rozmytych, takich jak klasteryzacja rozmyta. Dlatego szczególnie ważna z punktu widzenia metodologii badania danych jest próba zrozumienia ich potencjału jako ziaren informacji. W niniejszym opracowaniu przedstawimy szczególne przypadki wykorzystania innowacyjnej metody reprezentacji potencjału informacyjnego zmiennych za pomocą ziaren informacji. W serii eksperymentów numerycznych opartych zarówno na danych generowanych sztucznie, jak i danych ekologicznych dotyczących zmian dat przylotów ptaków w kontekście zmian klimatycznych, demonstrujemy skuteczność proponowanego podejścia przy użyciu klasycznych, a nie rozmytych miar budujących ziarna informacji.

3

Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine

Sriramakrishnan Padmanaban, Kalaiselvi Thiruvenkadam, Rajeswaran Rangasami

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 2

470--487

EN

The proposed work develops a rapid and automatic method for brain tumour detection and segmentation using multi-sequence magnetic resonance imaging (MRI) datasets available from BraTS. The proposed method consists of three phases: tumourous slice detection, tumour extraction and tumour substructures segmentation. In phase 1, feature blocks and SVM classifier are used to classify the MRI slices into normal or tumourous. Phase 2 contains fuzzy c means (FCM) algorithm to extract the tumour region from slices identified by phase 1. In addition, graphics processing unit (GPU) based FCM method has been implemented for reducing the processing time which is major overhead with FCM processing of MRI volumes. For phase 3, a novel probabilistic local ternary patterns (PLTP) technique is used to segment the tumour substructures based on the probability density value of histogram bins. Quantitative measures such as sensitivity, specificity, accuracy and dice values are used to analyses the performance of the proposed method and compare with state-of-art-methods. As post processing, the tumour volume estimation and 3D visualization were done for analyzing the nature and location of the tumour to the medical experts. Further, the availability of the GPU reduces the processing time up to 18 than serial CPU processing.

4

A hybrid approach for the delineation of brain lesion from CT images

Gautam A., Raman B., Raghuvanshi S.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 3

504--518

EN

Brain lesion segmentation from radiological images is the most important task in accurate diagnosis of patients. This paper presents a hybrid approach for the segmentation of brain lesion from computed tomography (CT) images based on the combination of fuzzy clustering using hyper tangent function as the robust kernel and distance regularized level set evolution (DRLSE) function as the edge based active contour method. Kernel based fuzzy clustering method divides the image into different regions. These regions can be used to find region of interest by using DRLSE algorithm to generate the optimal region boundary. The proposed method results in smooth boundary of the required regions with high accuracy of segmentation. In this paper, results are compared with standard fuzzy c-means (FCM) clustering, spatial FCM, robust kernel based fuzzy clustering (RFCM) and DRLSE algorithms. The performance of the proposed method is evaluated on CT scan images of hemorrhagic lesion, which shows that our method can segment brain lesion more accurately than the other conventional methods.

5

A new approach for the clustering using pairs of prototypes

Jezewski M., Czabanski R., Leski J., Horoba K.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

113--121

EN

In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.

6

Decision support for hardware and software, complex for precision farming tasks: student project

Ganchenko V. V

International Journal of Microelectronics and Computer Science

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2014

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Vol. 5, nr 1

5--13

EN

In the article algorithms for decision support for hardware and software complex are described. The complex is used for few precision farming tasks: data mining, data processing, decision making and control of fertilizers applying. The complex is designed to reduce costs and environmental burden on potato. The complex is based on processing aerial images photographs of potato ﬁelds.

7

Metoda oceny elementów infrastruktury drogowej z uwzględnieniem potrzeb i specyfiki różnych grup użytkowników

Kruszyna M.

Prace Naukowe Instytutu Inżynierii Lądowej Politechniki Wrocławskiej. Monografie

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2013

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Vol. 56, nr 24

1--168

PL

W monografii pod pojęciem infrastruktury drogowej autor rozważa obiekty budowlane, po których odbywa się transport osób i towarów w zakresie gałęzi transportu drogowego. Wprowadzono i zdefiniowano kluczowe dla prowadzonych rozważań pojęcia, takie jak: element infrastruktury drogowej, jego rodzaje (węzeł i odcinek międzywęzłowy) oraz części, grupa użytkowników, trasa, środek lokomocji, opis i ocena warunków ruchu. Dokonano zestawienia stanu wiedzy z zakresu poruszanych zagadnień w rozbiciu na: przegląd zagadnień z zakresu opisu elementu infrastruktury, znaczenie sygnalizacji drogowej w ocenie warunków ruchu i tendencje wykorzystania metod heurystycznych w ocenie elementów infrastruktury drogowej. Na bazie tego podsumowania określono autorski wkład w dziedzinę projektowania i oceny elementów infrastruktury drogowej. Sformułowano trzy tezy oraz sprecyzowano cel i zakres monografii. Podstawowym osiągnięciem monografii jest skonstruowanie uniwersalnej metody opisu elementów infrastruktury drogowej. Oprócz ujęcia tradycyjnych wielkości charakteryzujących geometrię drogi oraz ruch jej użytkowników uwzględniono rolę wymagań poszczególnych grup użytkowników. Analizy różnych grup użytkowników, w tym podróżujących w pojazdach transportu zbiorowego, pieszych i rowerzystów, wymagały stworzenia zestawu zunifikowanych wielkości i związanych z nimi jednostek. Skonstruowano metodę oceny elementu infrastruktury drogowej bazującą na autorskim modelu obejmującym opis elementu infrastruktury oraz poszczególne grupy użytkowników. W metodzie oceny wykorzystano oryginalny zestaw funkcji oraz uwarunkowań dopasowany do charakteru rozwiązywanych zadań. Metoda nadaje się do korygowania sposobów obliczania przepustowości i warunków oceny ruchu drogowego z rozwinięciem ich na wszystkie grupy użytkowników. Kompleksowy opis wszystkich części elementu infrastruktury pozwala także na ocenę wariantów realizacji, bądź zagospodarowania. Istotnym osiągnięciem pracy jest wykorzystanie metod grupowania rozmytego do kalibracji parametrów funkcji oceny oraz algorytmów genetycznych, jako nowoczesnych i efektywnych narzędzi rozwiązywania zadań oceny elementów infrastruktury. Na przykładach pokazano użyteczność skonstruowanego modelu oraz efektywność autorskiej metody oceny, także na tle dotychczas stosowanych metod i włączywszy w to zbudowane narzędzia komputerowe. Dodatkowo pokazano, że modyfikując konkretne elementy metody, takie jak: wagi, parametry funkcji oceny i jej postać, uwzględnia się różne stopnie priorytetów dla określonych grup użytkowników stosownie do formułowanych przez nich preferencji oraz oczekiwań decydentów.

EN

In the monograph, the term of road infrastructure means the construction object (structure), on which is the transportation of people and goods in the road transport branch. The key concepts, such as: road infrastructure element, its types and parts (node and intersection segment) groups of users, route, means of transport, description and evaluation of traffic conditions are introduced. The state of knowledge, divided into: a review of the infrastructure description issues, the importance of traffic signals in the evaluation of traffic conditions, as well as the trends in the use of heuristic methods in the evaluation of road infrastructure is pointed. On the basis of this summary, the contribution of Author to the field of design and evaluation of road infrastructure is determined. Three specific thesis, the aim and the scope of the monograph are formulated. The main achievement of the monograph is to provide the universal method for the description of road infrastructure. In addition to the recognition of traditional quantities characterizing the geometry of the road and the movement of the users, the role of the requirements of individual users group is considered. Analysis of different users groups, including travelers in vehicles or in public transport means, pedestrians and cyclists, required a unified set of the quantities and related units. The method of evaluation of road infrastructure elements, based on the model including description of the infrastructure and each groups of users is constructed. The original set of functions and conditions tailored to the nature of the considered problems been taken into account. The method is suitable for correct the ways to calculate the capacity and the traffic conditions for all the groups of users. A complex description of all the parts of an infrastructure element allows also the evaluation of options or management. An important achievement of the monograph is the use of the fuzzy clustering method to calibrate the parameters of the evaluation functions, and use of the genetic algorithms, as the modern and effective tools for solving the problems of infrastructure element evaluation. The examples demonstrate the usefulness and effectiveness of the constructed model, the author’s method of evaluation, also against the background of previous methods, and including the constructed computer tools. In addition, it is shown that by modifying the specific elements of the method, such as: weights, the parameters of the evaluation functions and their forms, take into account the different degrees of priority to certain groups of users formulated according to their preferences and expectations of policymakers.

8

Fuzzy clustering based methods for nystagmus movements detection in electronystagmography signal

Czabański R., Pander T., Horoba K., Przybyła T.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

277--283

EN

The analysis of optokinetic nystagmus (OKN) provides valuable information about the condition of human vision system. One of the phenomena that is used in the medical diagnosis is optokinetic nystagmus. Nystagmus are voluntary or involuntarily eye movements being a response to a stimuli which activate the optokinetic systems. The electronystagmography (ENG) signal corresponding to the nystagmus has a form of a saw tooth waveform with fast components related to saccades. The accurate detection of the saccades in the ENG signal is the base for the further estimation of the nystagmus characteristic. The proposed algorithm is based on the proper filtering of the ENG signal providing a waveform with amplitude peaks corresponding the fast eyes rotation. The correct recognition of the local maxima of the signal is obtained by the means of fuzzy c-means clustering (FCM). The paper presents three variants of saccades detection algorithm based on the FCM. The performance of the procedures was investigated using the artificial as well as the real optokinetic nystagmus cycles. The proposed method provides high detection sensitivity and allows for the automatic and precise determination of the saccades location in the preprocessed ENG signal.

9

Evaluation of distance between pedestrian crossings by students in one of the Polish cities

Kruszyna M.

Archives of Civil Engineering

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2013

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Vol. 59, nr 4

547--559

EN

In this paper, the distances between pedestrian crossings in twenty one places in the city of Wrocław, together with their evaluation by the researched groups of students, were analyzed. The database created from the collected questionnaires contains a set of two-dimensional variables: the distance between crossings and the rating of the students. The database set was analyzed using a fuzzy data mining approach to create particular clusters. Various numbers of clusters were analyzed, and the division of data into three clusters made it possible to relate the analysis to the LOS methodology. Each variable was enriched with a third dimension representing a membership value. The obtained evaluated distances are similar to values recommended in literature, although the distances highly evaluated by the students do not often occur in reality. This might suggest that there is the need to create new crossings, especially in the city centre, where pedestrian traffic is or should be important.

10

Detekcja sakkad w sygnale ENG z użyciem grupowania rozmytego

Pander T., Czabański R., Wróbel J., Horoba K., Przybyła T.

Aktualne Problemy Biomechaniki

|

2013

|

z. 7

137--142

PL

Sygnał elektrynystagmograficzny (ENG) z oczopląsem ma postać fali o piłokształtnym kształcie składającym się z fazy wolnej oraz szybkiej. Faza szybka to ruch sakkadyczny gałki ocznej. Skuteczna i dokładna detekcja sakkad ma kluczowe znaczenie w określeniu charakteru oczopląsu. W celu prawidłowej detekcji położenia sakkad sygnał ENG jest filtrowany a maksima lokalne są wykrywane za pomocą rozmytej metody c-średnich. Proponowany algorytm charakteryzuje się dużą czułością i pozwala na automatyczną i precyzyjną lokalizację sakkad w sygnale ENG.

EN

The electronystagmography (ENG) signal corresponding to nystagmus has a form of a saw tooth waveform with fast components related to saccades. The accurate detection of saccades in ENG signal is the base for the further estimation of the nystagmus characteristic. The proposed algorithm is based on the proper filtering of the ENG signal providing a waveform with amplitude peaks corresponding the fast eyes rotation. The correct recognition of the local maxima of the signal is obtained by the means of fuzzy c-means clustering (FCM). The proposed algorithm is highly sensitive and allows for the automatic and precise localization of the saccades in ENG signal.

11

Badanie sieci społecznych z wykorzystaniem algorytmu grupowania rozmytego

Sosnowski Z. A., Rembowicz K.

Symulacja w Badaniach i Rozwoju

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2012

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Vol. 3, nr 3

169--174

PL

W pracy przedstawiono propozycje, uzyskane wyniki oraz wypływające z nich wnioski dotyczące zastosowania teorii zbiorów rozmytych do analizy sieci społecznych. Wyniki symulacji pokazują, że proponowane podejście wykorzystujące własności zbiorów rozmytych sprawdza się bardzo dobrze w analizie spójnych sieci społecznych z niedużą liczbą klastrów.

EN

The paper presents proposals, the obtained results and the resulting conclusions concerning the use of fuzzy set theory to the analysis of social networks. The simulation results show that the proposed approach using fuzzy property works very well in the analysis of social networks consistent with a small number of clusters.

12

Rozmyta analiza skupień Punktów pomiarowych pobranej mocy biernej

Bielecki S.

Rynek Energii

|

2012

|

Nr 4

13--18

PL

Analiza skupień wartości pobranej mocy w kolejnych odcinkach czasu może dostarczyć informacji zarówno o charakterystycznych wycinkach profili poboru mocy, jak i ilości typów całych profili. W artykule przedstawione zostały rezultaty działania zaimplementowanego algorytmu grupowania wektorów danych. Analizie poddano dane pomiarowe pobranej w 15-minutowych odcinkach czasu mocy biernej przez pojedynczych odbiorców w ciągu roku, grupując zarówno całe profile dobowe, jak i poszczególne punkty czasowe poboru mocy w ciągu doby. W przypadku grupowania całych profili, najlepsze podziały uzyskano dla liczby grup z zakresu od 2 do 6, większy rozrzut liczby klas uzyskano przy grupowaniu punktów w obrębie doby. Opracowany algorytm opierał się na rozmytej metodzie klasteryzacji c-średnich z dodatkowymi elementami poprawiającymi jego funkcjonalność. Zadanie polegało na takim podziale zbioru obserwacji z wielowymiarowej przestrzeni, aby dane przyporządkowane do jednej grupy były w większym stopniu podobne do siebie niż do jakiejkolwiek innej. Rozmyte podejście umożliwia częściową przynależność poszczególnych elementów do wielu grup. W pracy przebadano różne rodzaje wskaźników oceny jakości grupowania. Wyniki przeprowadzonych badań pozwalają stwierdzić, że najbardziej przydatne okazują się wskaźniki jakości Fukuyamy-Sugeno oraz wskaźnik rozmytych rozproszeń.

EN

A cluster analysis of data of power consumption in terms can inform about the characteristic points in power consumption profiles as well as a number of models of profiles. In this paper are presented the results of cluster algorithm application. By the analysis have been dealt with measurements data of reactive power consumption by single customers and clustering day-profile as well as individual term consumption have been executed. The best results of clustering have been obtained by clustering of profiles for the number of clusters between 2 and 6, more dispersed range has been observed by clustering of term consumption. In the adapted algorithm has been used Fuzzy C-Means with addition elements improving functionality. In the paper the test results of various indexes of cluster validity have been presented. The experiments have shown that Fukuyama-Sugeno index and Fuzzy Dispersion index are more useful.

13

Segmentation of biomedical signals using an unsupervised approach

Przybyła T., Wróbel J., Czabański R., Horoba K., Pander T., Momot M.

Journal of Medical Informatics & Technologies

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2012

|

Vol. 19

125--131

EN

The paper presents an unsupervised approach to biomedical signal segmentation. The proposed segmentation process consists of several stages. In the first step, a state-space of the signal is reconstructed. In the next step, the dimension of the reconstructed state-space is reduced by projection into principal axes. The final step involves fuzzy clustering method. The clustering process is applied in the kernel-feature space. In the experimental part, the fetal heart rate (FHR) signal is used. The FHR baseline and the acceleration or deceleration patterns are the main signal nonstationarities but also the most clinically important signal features determined and interpreted in computer-aided analysis.

14

Application of modified fuzzy clustering to medical data classification

Jeżewski M.

Journal of Medical Informatics & Technologies

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2011

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Vol. 17

51--57

EN

Classification plays very important role in medical diagnosis. This paper presents fuzzy clustering method dedicated to classification algorithms. It focuses on two additional sub-methods modifying obtained clustering prototypes and leading to final prototypes, which are used for creating the classifier fuzzy if-then rules. The main goal of that work was to examine a performance of the classifier which uses such rules. Commonly used including medical benchmark databases were applied. In order to validate the results, each database was represented by 100 pairs of learning and testing subsets. The obtained classification quality was better in relation to the one of the best classifiers - Lagrangian SVM and suggests that presented clustering with additional sub-methods are appropriate to application to classification algorithms.

15

An approach to unsupervised classification

Przybyła T., Pander T., Horoba K., Kupka T., Matonia A.

Journal of Medical Informatics & Technologies

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2011

|

Vol. 17

105--111

EN

Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the unsupervised classification with the popular classifiers. The fuzzy clustering method is used to create a learning set. The learning set includes only these patterns that are the best representative of each class in the input dataset. The numerical experiment uses an artificial dataset as well as the medical datasets (PIMA, Wisconsin Breast Cancer) and illustrates the usefulness of the proposed method.

16

Generalized fuzzy clustering method

Przybyła T., Roj D., Jeżewski J., Matonia A.

Journal of Medical Informatics & Technologies

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2010

|

Vol. 16

69--76

EN

This paper presents a new hybrid fuzzy clustering method. In the proposed method, cluster prototypes are values that minimize the introduced generalized cost function. The proposed method can be considered as a generalization of fuzzy c–means (FCM) method as well as the fuzzy c–median (FCMed) clustering method. The generalization of the cluster cost function is made by applying the Lp norm. The values that minimize the proposed cost function have been chosen as the group prototypes. The weighted myriad is the special case of the group prototype, when the Lp norm is the L2 (Euclidean) norm. The cluster prototypes are the weighted meridians for the L1 norm. Artificial data set is used to demonstrate the performance of proposed method.

17

Parallel fuzzy clustering for linguistic summaries

Smolińska M. K., Sosnowski Z. A.

Zeszyty Naukowe Politechniki Białostockiej. Informatyka

|

2009

|

Z. 4

139-150

PL

Z podsumowaniem lingwistycznym, jak i z predykatem rozmytym związana jest wartość prawdy. Możemy więc podsumowań lingwistycznych używać jako predykatów rozmytych. Podsumowanie postaci większość obiektów w populacji P jest podobna do obiektu oi wykorzystać możemy do znajdowania typowych wartości w populacji P, które to wykorzystuje rozmyty algorytm grupujący. Wadą tego algorytmu jest jego duża złożoność obliczeniowa. W celu przetwarzania dużej liczby danych zaimplementowaliśmy ten algorytm równolegle, korzystając ze standardu MPI do komunikacji między procesami działającymi na różnych procesorach. W tej pracy przedstawiamy algorytm równoległy i wyniki eksperymentów.

EN

The linguistic summaries have the associated truth value so they can be used as predicates. We use summaries of the form ”most objects in population P are similar to oi” to find typical values in population P. Then typical values are used in fuzzy clustering algorithm. Disadvantage of this algorithm is its complexity. For the purpose of processing the huge number of data, we decided to use parallel computing mechanism to implement this algorithm, and run it on the cluster machine. We use MPI (Message Passing Interface) to communicate between processes, which work on different processors. This paper presents this parallel algorithm and some results of experiments.

18

An automatic hybrid method for retinal blood vessel extraction

Yang Y., Huang S., Rao N.

International Journal of Applied Mathematics and Computer Science

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2008

|

Vol. 18, no 3

399-407

EN

The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information is suppressed. The fuzzy clustering algorithm is then employed to the previous enhanced image for segmentation. After the fuzzy segmentation, a purification procedure is used to reduce the weak edges and noise, and the final results of the blood vessels are consequently achieved. The performance of the proposed method is compared with some existing segmentation methods and hand-labeled segmentations. The approach has been tested on a series of retinal images, and experimental results show that our technique is promising and effective.

19

Podsumowania lingwistyczne z grupowaniem rozmytym

Smolińska M. K., Sosnowski Z. A.

Zeszyty Naukowe Politechniki Białostockiej. Informatyka

|

2007

|

Z. 2

141-154

PL

W pracy przedstawiono zastosowanie podsumowania lingwistycznego jako predykatu rozmytego do wyznaczania obiektów z typową wartością atrybutu lub zbioru atrybutów. W rozmytym algorytmie grupującym wykorzystana jest populacja z wyznaczoną ze względu na dany atrybut typowością obiektów. Wyniki działania tego algorytmu oraz jego zmodyfikowanej postaci zostały przedstawione na przykładzie populacji, której obiektami są piksele obrazu.

EN

This paper presents linguistic summary as a fuzzy predicate, which is used to find, objects with typical values of an attribute or a set of attributes. In the fuzzy clustering algorithm we use population with given typicality of objects for selected attribute. We present the results of this algorithm and its modification basing on an example with population of pixels in image.

20

Knowledge-based clustering as a conceptual and algorithmic environment of biomedical data analysis

Pedrycz W., Gacek A.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

KB13--22

EN

While a genuine abundance of biomedical data available nowadays becomes a genuine blessing, it also posses a lot of challenges. The two fundamental and commonly occurring directions in data analysis deal with its supervised or unsupervised pursuits. Our conjecture is that in the area of biomedical data processing and understanding where we encounter a genuine diversity of patterns, problem descriptions and design objectives, this type of dichotomy is neither ideal nor the most productive. In particular, the limitations of such taxonomy become profoundly evident in the context of unsupervised learning. Clustering (being usually regarded as a synonym of unsupervised data analysis) is aimed at determining a structure in a data set by optimizing a given partition criterion. In this sense, a structure emerges (becomes formed) without a direct intervention of the user. While the underlying concept looks appealing, there are numerous sources of domain knowledge that could be effectively incorporated into clustering mechanisms and subsequently help navigate throughout large data spaces. In unsupervised learning, this unified treatment of data and domain knowledge leads to the general concept of what could be coined as knowledge-based clustering. In this study, we discuss the underlying principles of this paradigm and present its various methodological and algorithmic facets. In particular, we elaborate on the main issues of incorporating domain knowledge into the clustering environment such as (a) partial labelling, (b) referential labelling (including proximity and entropy constraints), (c) usage of conditional (navigational) variables, (d) exploitation of external structure. Presented are also concepts of stepwise clustering in which the structure of data is revealed via a series of refinements of existing domain granular information.