An Iterative Improvement Procedure for Hierarchical Clustering
نویسندگان
چکیده
We describe a procedure which finds a hierarchical clustering by hillclimbing. The cost function we use is a hierarchical extension of the k-means cost; our local moves are tree restructurings and node reorderings. We show these can be accomplished efficiently, by exploiting special properties of squared Euclidean distances and by using techniques from scheduling and VLSI algorithms.
منابع مشابه
Large Scale Circuit Partitioning with Loose/Stable Net Removal and Signal Flow Based Hierarchical Clustering
20 demonstrate the superiority of our approach over the best available iterative partitioning algorithm CLIP-PROP f DuDe96] with an average improvement of 7.1% in terms of minimum cutsize. In this paper, we introduced the notion and importance of loose net and provided a loose net removal (LR) algorithm to remove loose nets from the cutset. The stable net removal algorithm ShNK95] was then inco...
متن کاملCOMPARISON WITH llULTIDIMENSIONAL SCALING
A method of hierarchical clustering for relational data is presented, which begins by forming a new square matrix of productmoment correlations between the columns (or rows) of the original data (represented as an n x m matrix). Iterative application of this simple procedure will in general converge to a matrix which may be permuted into the blocked form [_~ -~J. This convergence property may b...
متن کاملAn iterative method for tri-level quadratic fractional programming problems using fuzzy goal programming approach
Tri-level optimization problems are optimization problems with three nested hierarchical structures, where in most cases conflicting objectives are set at each level of hierarchy. Such problems are common in management, engineering designs and in decision making situations in general, and are known to be strongly NP-hard. Existing solution methods lack universality in solving these types of pro...
متن کاملمقایسه نتایج خوشهبندی سلسله مراتبی و غیرسلسله مراتبی پروتئینهای مرتبط با سرطانهای مری، معده و کلون براساس تشابهات تفسیر هستیشناسی ژنی
Background and Objective: Using proteomic methodologies and advent of high-throughput (HTP) investigation of proteins has created a need for new approaches in bioinformatics analysis of experimental results. Cluster analysis is a suitable statistical procedure that can be useful for analyzing these data sets. Materials and Methods: In this research study, the identified proteins associated wi...
متن کاملK-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation
The K-means Iterative Fisher (KIF) algorithm is a robust, unsupervised clustering algorithm applied here to the problem of image texture segmentation. The KIF algorithm involves two steps. First, K-means is applied. Second, the K-means class assignments are used to estimate parameters required for a Fisher linear discriminant (FLD). The FLD is applied iteratively to improve the solution. This c...
متن کامل