Clustering & Classification (chapter 15)
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1 Clustering & Classification (chapter 5) Kai Goebel Bill Cheetham RPI/GE Global Research Outline k-means Fuzzy c-means Mountain Clustering knn Fuzzy knn Hierarchical Methods Adaptive Clustering 9/9/003
2 3 Preliminaries Partitioning of data into several groups s.t. similarity within group is larger than that among groups Clustering = Classification Need similarity metric Need to normalize data Supervised vs. unsupervised clustering issues Unsupervised: labeling cost high (large # of data, costly experiments, data not available, ) Understand internal distribution Preprocessing for classification 9/9/003 k-means Clustering Partitions data into k (or c) groups Finds cluster centers Dissimilarity measure: e.g., Euclidean distance q ( m, i ) = ( m i ) d x c x c p= s.t. cost function is minimized c where c=number of clusters n=data within cluster q=number of dimensions c n p c n q ( m, i ) ( m i ) J = J = d x c = x c i i= i= m=, xm G i i= m=, xm G i p= p 4 9/9/003
3 Other Dissimilarity Measures ( ) T xy d x, y = x y x y T x y d ( x, y) = T T T x y + y y y x (binary x, y) ( ) d x, y = T x y x y d x y x y x y i= N m (, ) = = ( i i ) m m 5 9/9/003 k-means Algorithm Initialize cluster centers (randomly?) Check whether data are closest to cluster center Compute cost function (stop if below threshold) Update cluster centers ci = where Gi n x k k, xk G i (mean of all vectors in group i) G i is the number of elements in cluster G i Go to second step Issues: selection of location and number of clusters 6 9/9/003
4 Cluster class exercise height shirt color Fuzzy C-means Allow points to belong partly to several clusters why? 8 9/9/003
5 Butterfly Example Fuzzy Clustering Formulate optimization problem n c m min J( U, V ) = ( uik ) d ( xk, vi ) k = i= Calculate membership values u ik = d c j= ( x, v ) d x v i k ( j, k ) m m Update centroid v i n ( uik ) k = = n m ( uik ) k = x m k
6 Butterfly Simulation 9/9/003 Powerpoint Butterfly Animation 9/9/003
7 Many Simulations for Butterfly 50 Runs from random starting points centroids always settle at the same points Fuzzy Cluster in 3-D Space
8 Mountain Clustering No need to set number of clusters a priori simple computationally expensive can be used to determine number of clusters for c-means Mountain Clustering: steps form a grid on the data space; intersections are candidates for cluster centers. construct a mountain function representing data density m( v) = N i= e v xi σ (each data point contributes to the height) sequentially destruct the mountain function: make dent where highest value is m new ( v) = m( v) m( c) e v c i β subtracted amount inversely proportional to distance between v and c and height m(c )
9 D data for Mountain Clustering. mountain function with b) σ=0.0; c) 0.; d) 0.. destruction with β= b) first; c) second; d) third knn Algorithm Looks for k nearest neighbors (knn) to classify data Assigns class based on majority among knn Supervised method - needs labeled data for training 8 9/9/003
10 Crisp knn Algorithm Compute distance from data point to labeled samples IF knn have not been found yet THEN include data point ELSEIF a labeled sample is closer to the data point than any other knn then replace the farthest with the new one Deal with ties Repeat for the next labeled sample 9 9/9/003 Example 0 9/9/003
11 Fuzzy knn Assigns class membership Computationally simple Assign membership based on distance to knn and their memberships in classes 9/9/003 Fuzzy knn Algorithm Compute distance from data point to labeled samples If knn have not been found yet then include data point Else, if a labeled sample is closer to the data point than any other knn then replace the farthest with the new one Compute membership (inverse of distances from nn and their class memberships) Repeat for the next labeled sample u ( x) = i k uij x x j= m j k j= x x m j 9/9/003
12 Hierarchical Clustering Merge method: start with each x i as a cluster merge the nearest pairs until #of clusters = Split method: start with # of clusters = split until predefined goal is reached 3 9/9/003 Classifying Turbine Anomalies with Adaptive Fuzzy Clustering Objective: track behavior of engines measure system parameters trend analysis for change detection Challenges: large amounts of noise changing operating conditions corrections with first principle models or regression models work only to some extent changes of schedules, maintenance, etc., which are not necessarily known to the analyst
13 Trending Monitor data and report observations indicative of abnormal conditions Issues: definition of abnormality not crisp step changes different slope combination of events trade-off between false positives and false negatives trade-off also with time to recognition tool performance not the same for all conditions of interest noise in data will influence quality of reporting Trending of Shift Changes Problem: detect abnormal shifts in process data noise masks shifts slow drifts are superimposed on data Solution: Multivariate adaptive fuzzy clustering Self-learning of normal drift Capability to recognize abnormal changes 0 0 de GT w f N
14 Components: Shift Detection Tool Completeness checker and new data checker Normalizer Classifier (fuzzy knn) Calculator of statistical propertiers (performs learning) Adaptor (tracks normal wear) Persistency checker (vigilance) Composite alert score detector (generates alert) Cluster Adaptation tracks system changes
15 Raw Data Output Tool Shift Detection 0 0 w f Example Data de G T N m ulti-variate view for last slide
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