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Using Semi-supervised Clustering for Neurons Classification
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

We wish to understand brain; discover its sophisticated ways of calculations to invent improved computational methods. To decipher any complex system, first its components should be understood. Brain comprises neurons.

Neurobiologists use morphologic properties like “somatic perimeter”, “axonal length”, and “number of dendrites” to classify neurons. They have discerned two types of neurons: “interneurons” and “pyramidal cells”, and have a consensus about five classes of interneurons: PV, 2/3, Martinotti, Chandelier, and NPY. They still need a more refined classification of interneurons because they suppose its known classes may contain subclasses or new classes may arise. This is a difficult process because of the great number and diversity of interneurons and lack of objective indices to classify them.

Machine learning—automatic learning from data—can overcome the mentioned difficulties, but it needs a data set to learn from. To meet this demand neurobiologists compiled a data set from measuring 67 morphologic properties of 220 interneurons of mouse brains; they also labeled some of the samples—i.e. added their opinion about the sample’s classes.

This project aimed to use machine learning to determine the true number of classes within the data set, classes of the unlabeled samples, and the accuracy of the available class labels. We used K-means, seeded K-means, and constrained K-means, and clustering validity techniques to achieve our objectives. Our results indicate that: the data set contains seven classes; seeded K-means outperforms K-means and constrained K-means; chandelier and 2/3 are the most consistent classes, whereas PV and Martinotti are the least consistent ones.

Place, publisher, year, edition, pages
2013. , 40 p.
Keyword [en]
machine learning, clustering, semi-supervised learning, semi-supervised clustering, data mining, number of clusters
National Category
Computer Science
URN: urn:nbn:se:liu:diva-92719ISRN: LIU-IDA/LITH-EX-A--13/019--SEOAI: diva2:621722
Subject / course
Computer and information science at the Institute of Technology
2013-04-26, 16:10 (English)
Available from: 2013-05-27 Created: 2013-05-16 Last updated: 2015-02-18Bibliographically approved

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