............................................................................................................................. iii Acknowledgments.............................................................................................................. iii Vita ..................................................................................................................................... iv Table of

Developing a Social Support Measurement Instrument: A Methodological Approach to Measuring Undergraduate Perceptions of Social Support

Kumeleme analizi nesnelerin dogal gruplarini bulmak icin kullanilan bir yontemdir. Kumeleme yapilirken kume ici homojenlik ile kumeler arasi heterojenligin yuksek olmasi istenir. Literaturde, kategorik verileri kumelemek icin cok fazla yontem yoktur ve var olanlarin hangisinin en iyi oldugu ile ilgili kesin bir bilgi bulunmamaktadir. Veri sayisina ve veri yapisina gore her bir yontemin birbirine ustunlukleri ve eksiklikleri vardir. Ayrica iyi bir kumeleme yapmak icin kullanilacak degisken sayisi buyuk onem tasimaktadir. Bu calismada kategorik verilerin kumelenmesi ile ilgilenildi. Hiyerarsik kumeleme tekniklerinden tek baglanti teknigi, tam baglanti teknigi, ortalama baglanti teknigi ve bolmeli kumeleme tekniklerinden K-modes algoritmasi kullanilarak kumeleme analizi yapildi ve sonuclar karsilastirildi. Analiz sonuclarina gore veri sayisi buyudukce kumeleme performansi hiyerarsik tekniklerde azalirken K-modes algoritmasinda arttigi tespit edildi.

/pdf/kategorik-verilerde-kumeleme-icin-farkli-algoritmalarin-9pxzyhzwdr.pdf

Kategorik Verilerde Kümeleme İçin Farklı Algoritmaların Karşılaştırılması

This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS) data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.

/pdf/analyses-of-crime-patterns-in-nibrs-data-based-on-a-novel-8cl4juov0t.pdf

Analyses of crime patterns in NIBRS data based on a novel graph theory clustering method: Virginia as a case study.

/pdf/clustering-categorical-data-using-data-summaries-and-2ozm0atdxu.pdf

Clustering Categorical Data Using Data Summaries and Spectral Techniques

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Clustering Categorical Data Using Data Summaries and Spectral Techniques