Ensemble Clustering for Biological Datasets

Pirim, Harun; Şeker, Şadi Evren

doi:10.5772/49956

Cited by 4 publications

(5 citation statements)

References 34 publications

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“…Ağır travmat k olaylar sonucu akıl sağlığının y t r lmes buna b r örnekt r. Ancak b yoloj k zeka, nsan karakter n n sah p olduğu bel rl kab l yetlere bağlı olarak çeş tlenmekted r. Örneğ n görme, ş tme ve dokunma duyularıyla olduğu kadar sezg lerle de düşünmek ve b r yargıya varmak mümkündür. Kabul gören b r tar fe göre toplamda sek z zeka türünden bahsed lmekted r 14 ; bunlar matematiksel zeka, pratik zeka, edebi ve linguistik zeka, şekilci zeka, müzik zekası, duygusal zeka, fiziksel zeka ve evrensel zekadır. Her nsanda farklı b r zeka türü ve buna bağlı olarak da düşünme tarzı söz konusur.…”

Section: B B Yoloj K Zeka İle Mukayese Ve Yapay Zeka çEş Tlerunclassified

The Legal Character of Arti̇fi̇ci̇al Intelli̇gence’s Products and the Intellectual Work Concept

Balta¹

2020

Ankara Hacı Bayram Veli Üniversitesi Hukuk Fakültesi Dergisi

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21. yüzyıl itibariyle şüphesiz teknolojinin gelişmesine ve teknolojik imkan ve araştırmaların artış göstermesine bağlı olarak, adından çokça söz ettiren yapay zeka, insan hayatındaki anlam ve önemini her geçen gün biraz daha tutarlı ve kararlı bir biçimde ortaya koymaktadır. Gündelik hayatta temizlik amacıyla kullanılan robotlar kadar, sağlık sektörünün adeta vazgeçilmezi haline gelen tıbbi klinik uygulamalar şeklinde de belirebilen yapay zeka, insan hayatının bir parçası haline gelmiş, insan yaşam tarzıyla adeta bütünleşmiştir. Öyle ki, insan zekası modelleştirilerek geliştirilen yapay zekanın, ayrıca insan benzeri yeteneklerle çeşitli ürünleri de resim çizmek, şarkı söylemek, dans etmek, bestelemek suretiyle üretebiliyor olması, özellikle fikri mülkiyet hukuku bağlamında bu ürünlerin niteliğine yönelik tartışmalara sebebiyet vermektedir. Bu ürünlerin fikri mülkiyet hukuku kapsamında eser olarak değerlendirilip değerlendirilemeyeceği ancak eser kavramının tespiti ile mümkündür. Zira fikri mülkiyet hukukunun odak noktasını eser kavramı ve unsurları teşkil etmektedir. Bu çalışmanın amacı öncelikle çeşitli yönleriyle yapay zekayı tanımlamak, yapay zeka ürünlerini örneklendirdikten sonra bu ürünlerin hukuki niteliğini değerlendirebilmek adına eser kavramını incelemektir.

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Section: B B Yoloj K Zeka İle Mukayese Ve Yapay Zeka çEş Tlerunclassified

The Legal Character of Arti̇fi̇ci̇al Intelli̇gence’s Products and the Intellectual Work Concept

Balta¹

2020

Ankara Hacı Bayram Veli Üniversitesi Hukuk Fakültesi Dergisi

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“…It also provides for a visualization tool to examine cluster number, membership, and boundaries. In this sense ensemble clustering is a potential approach to generate more accurate clusters than might be possible using an individual clustering approach [15]. It generally involves two major tasks as Generation step in which generating several clustering solutions by applying clustering algorithm is done and the Consensus step through which final cluster partition is produced.…”

Section: Cluster Ensemble Paradigmmentioning

confidence: 99%

“…The general outlier of the cluster ensemble is done by achieving the solutions from the different base clustering which are then aggregated to form a final partition [13]. This Meta level approach involves these two major tasks of generating a cluster ensemble and then producing a final partition normally referred as the consensus function [15] [13]. Precisely the great challenge in clustering ensemble is the definition of most suitable consensus function which is capable of improving the consequences of single clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining

Sarumathi¹,

Shanthi²,

Sharmila³

2013

IJCA

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Over the past decades, a prevalent amount of work has been done in the data clustering research under the unsupervised learning technique in Data mining. Moreover a myriad of algorithms and methods has been proposed focusing on clustering different data types, representation of cluster models, and accuracy rates of the clusters. However no single clustering algorithm proves to be the most efficient in providing best results. Accordingly in order to find the solution to this issue a new technique, called Cluster ensemble method was bloomed. This cluster ensemble is a good alternative approach for facing the cluster analysis problem. The main aspire of the cluster ensemble is to combine different clustering solutions in such a way to achieve accuracy and to improve the quality of individual data clustering. Due to the substantial and unremitting development of the new methods in the sphere of data mining, it is obligatory to make a critical analysis of the existing techniques and the future novelty. This paper reveals the comparative study of different cluster ensemble methods along with their features, systematic working process and the average accuracy and error rates of each ensemble methods. Consequently this theoretical and comprehensive analysis will be very useful for the community of clustering practitioners and also helps in deciding the most suitable one to rectify the problem in hand.

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“…To analyze these signs, where the labels of the unprocessed data are unknown, computational intelligence analysis is needed. Hence, efficient machine learning paradigms [22,23] can be used to classify such biological anomalous behavior datasets and help generate timed alerts. Here, the alert refers to a warning that can assist in the execution of safety measures and thereby avoid loss of life and property to humanity.…”

Section: Introductionmentioning

confidence: 99%

Did They Sense it Coming? A Pipelined Approach for Tsunami Prediction Based on Aquatic Behavior Using Ensemble Clustering and Fuzzy Rule-Based Classification

et al. 2020

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Tsunami is one of the real feelings of dread among humanity. Designing an early and effective Tsunami Warning System (TWS) is an immediate goal, for which the scientific community is working. Underwater seismic responses sensed by different numerical expository techniques have resulted in various cautionary frameworks proving successful in predicting tsunamis. However, multiple instances in the past where these warning systems have failed to generate alerts in time, has raised concerns to design even more efficient, diverse, and multidisciplinary warning methods or systems. However, there have been many instances in the past where these warning systems have failed to generate alerts in time, raising concerns about designing/implementing more efficient, diverse, and multidisciplinary warning methods or systems. Therefore, we propose a sequenced (EC G F C) approach for designing a TWS, based on Ensemble Clustering (EC G) and Classification for categorizing anomalous behavior in response to seismic perturbations, taking three aquatic animal behavioral datasets: Turtle, Earthworm, and Fish, as the input(s). EC G uses an existing state-of-the-art method bagged with Gaussian mixture model to label the dynamically changing behavioral data. The paper compares the results of the clustering ensemble used with baseline clustering methods on three behavior datasets as well as four benchmark datasets. The proposed sequenced (EC G F C) method is finally compared on three classification error metrics: MSE, MAE, and SMAPE on behavioral and existing ensemble frameworks in the state-of-the-art.

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Ensemble Clustering for Biological Datasets

Cited by 4 publications

References 34 publications

The Legal Character of Arti̇fi̇ci̇al Intelli̇gence’s Products and the Intellectual Work Concept

The Legal Character of Arti̇fi̇ci̇al Intelli̇gence’s Products and the Intellectual Work Concept

A Comparative Analysis of Different Categorical Data Clustering Ensemble Methods in Data Mining

Did They Sense it Coming? A Pipelined Approach for Tsunami Prediction Based on Aquatic Behavior Using Ensemble Clustering and Fuzzy Rule-Based Classification

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