Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Two-phase clustering process for outliers detection
Pattern Recognition Letters
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
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This paper applies a two-phase methodology to cluster 366 records of the wire bond machines for a Taiwanese IC packaging foundry, where six attributes of each machine are chosen to cluster. The purpose of clustering in this paper is to help the foundry more effectively assign a family of machines to orders that appear in various forms such as emergent or quality-demanding and are highly dynamic in nature. Given the clusters, we use the technique of parallel coordinates to plot each attribute's centers in clusters so that the foundry can take advantage of visualization to determine what machines can be assigned to orders. To plot these centers, we map the input values into the range of -1 and +1 so that proper parallel coordinates can be used. Originating from the graphs, we also compare the same machines appearing in clusters that are produced by applying different clustering methods. These identical machines form the important basis to support the clustering results for the management of machines.