Clustering Methods for Exploratory Data Mining
Explore the world of clustering in data mining with a focus on wine data analysis. Learn about various methods like MDS, Isomap, Kmeans, Ncut, and more. Understand the motivation behind clustering and its real-world applications. Dive into the dimensions of wine data and different distance calculations to enhance object separation. Discover the importance of Mahalanobis distance and delve into 3D MDS visualization.
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Presentation Transcript
Wine Clustering Ling Lin
Contents Motivation Data Dimensionality Reduction-MDS, Isomap Clustering-Kmeans, Ncut, Ratio Cut, SCC Conclustion Reference
Motivation Clustering is a main task of exploratory data mining Make market Segementation, marketing strategies Document Clustering Target appropriate treatment to patients with similar response patterns Image segementation Apply clustering methods to a real data
Data Wine data Source of the data set : Machine Learning Repository , University of California, Irvine. Data sample size : 14 variables and 178 observations in 3 classes : different cultivar Variables : 1) Alcohol 2) Malic acid 3) Ash 4) Alcalinity of ash 5) Magnesium 6) Total phenols 7) Flavanoids 8) Nonflavanoid phenols 9) Proanthocyanins 10)Color intensity 11)Hue 12)OD280/OD315 of diluted wines 13)Proline
MDS Can I seperate objects better? ---> change the ways to find the distances
Cityblock(L1) Distance Chebychev Distance Cosine Distance Mahalanobis Distance
Distances Euclidean Distance-Straight line distance between two points. ? 2 ? = ?? ?? ?=1 City-block Distance- (L1 Distance) Sum of the distances of two points in any coordinate dimension. ? ? = ?? ?? ?=1
Distances Chebychev Distance-(Chessboard Distance) The greatest distance of two points difference in any coordinate dimension. ? = max ??1 ??1, ??2 ??2, ??3 ??3 ??? ??? Cosine Distance- The cosine of the angle between two vectors ? ? ? ? = cos(?) = ?
Distances Mahalanobis Distance-The dissimilarity of two vectors. S is the covariance matrix. ? ??? 1? ? ? = Euclidean Distance = c b City-block Distance = a+b a Chebychev Distance = max(a,b) = a c Cosine Distance = cos( )
Isomap Cosine Mahalanobis
Isomap Cosine Mahalanobis
Kmeans Clustering Error rate = 0.03
True Labeled Kmeans Clustering Clustering Comparison Normalized Cut Ratio Cut SCC
Conclusion Dimensionality Reduction- Different methods for calculating distances and reducing dimension --->Wine data V X 3D MDS Cosine Distance Mahalanobis 2D MDS Cosine Distance Mahalanobis Isomap make Mahalanobis distance a better display
Conclusion Clustering: Kmeans= Rcut SCC Ncut Ncut and Rcut : consider both inter and intra cluster connections. However, in this dataset, the intra cluster connections are weak.
