PPT-Issue 2: Clustering of Folk Cultures

Isolation promotes cultural diversity Himalayan art Influence of the physical environment Distinctive food preferences Folk housing US folk house forms Isolation and Cultural Diversity Folk culture typically has unknown or multiple origins among groups living in relative isolation . Epics. The art of story-telling has been cultivated in all ages and among all nations of which we have any record; it is the outcome of an instinct implanted universally in the human mind.. —Edwin Sidney Hartland. 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 . . Read pg. 108 in book. Culture and Customs. People living in other locations often have extremely different social customs. . Geographers ask why such differences exist and how social customs are related to the cultural landscape.. Leonard Cohen (1934- ). Gordon Lightfoot (1938 ). Bruce Cockburn (1945- ). Joni Mitchell (1943- ). Stan Rogers (1949-1983). Anne Murray. Buffy Sainte Marie (1941- ). Neil Young. Classic Folk Musicians. 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 . What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. Key Issue 1: Where do folk and pop cultures originate and diffuse?.  . a repetitive act that a particular individual performs.. a repetitive act that a particular group performs.. . the culture traditionally practiced primarily by small, homogenous groups living in isolated rural areas.. 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 . 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. 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.. Randomization tests. Cluster Validity . All clustering algorithms provided with a set of points output a clustering. How . to evaluate the “goodness” of the resulting clusters?. Tricky because .