PPT-Fuzzy Clustering of Software Metrics

Paper by Scott Dick and Abraham Kandel Presentation by Craig Castaneda Metrics Quality and Resources Developers monitor various attributes of the development process through metrics in an effort to improve the quality of the final product. Frank Costanzo – MAT 7670 Spring 2012. Introduction. Fuzzy logic began with the introduction of Fuzzy Set Theory by . Lotfi. . Zadeh. in 1965.. Fuzzy Set. Sets whose elements have degrees of membership. Fuzzy Logic. Lotfi. . Zadeh. (professor at UC Berkeley) wrote his original paper on . fuzzy set theory. . In various occasions, this is what he said…. “Fuzzy logic is a means of presenting problems to computers in a way akin to the way humans solve them”. Lect. 5 . Fuzzy Logic Control. Basil . Hamed. Electrical Engineering . Islamic University of Gaza. Content. Classical Control. Fuzzy Logic Control. The Architecture of Fuzzy Inference . Systems. Fuzzy Control Model. Abby . yinger. Definitions. Set – any well defined collection of objects. An object in a set is called an element or member of that set. .. Crisp Sets – these are sets that only have values of 0 (‘False’) and 1 (‘True’).. Lect. 5 . Fuzzy Logic Control. Basil . Hamed. Electrical Engineering . Islamic University of Gaza. Content. Classical Control. Fuzzy Logic Control. The Architecture of Fuzzy Inference . Systems. Fuzzy Control Model. Don McGreal. don.mcgreal@improvingenterprises.com. @. donmcgreal. . linkedin.com. /in/. donmcgreal. Agenda. Types of Metrics. Problems with Metrics. Metrics in Software Development. Evidence-Based Management. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . Steven Borg, Principal ALM Consultant . Northwest Cadence. Steven.Borg@nwcadence.com. Every ‘best in class’ company measures software quality. . There are no exceptions. . If your company does not do this it is not an industry leader and there is a good chance that your software quality levels are marginal at best. . Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Syntax. Using an ARDS detection automaton as a working example. Jeroen S. DE BRUIN. 1,2. ,. . Heinz STELTZER. 3. , . Andrea RAPPELSBERGER. 1. , . and . Klaus-Peter ADLASSNIG. 1,2. 1 . Section for Artificial Intelligence and Decision Support, . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values.. 1. Khurshid Ahmad, . Professor of Computer Science,. Department of Computer Science. Trinity College,. Dublin-2, IRELAND. October . 5th, 2011.. https. ://. www.cs.tcd.ie/Khurshid.Ahmad/Teaching/Teaching.html.