Fitting Tree Metrics: Hierarchical Clustering and Phylogeny
نویسندگان
چکیده
منابع مشابه
Hierarchical Clustering via Spreading Metrics
We study the cost function for hierarchical clusterings introduced by [Dasgupta, 2016] where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in [Dasgupta, 2016] that a top-down algorithm returns a hierarchical clustering of cost at most O (αn log n) times the cost of the optimal hierarchical clustering, where ...
متن کاملFitting Distances by Tree Metrics with Increment Error
The L p-min increment t and L p-min increment ultrametric t problems are two popular optimization problems arising from distance methods for reconstructing phyloge-netic trees. This paper gives the following results: 1. An O(n 2) approximation algorithm with ratio-3 for L 1-min increment t. 2. A ratio-O(n 1=p) approximation algorithm for L p-min increment ultrametric t. 3. The neighbor-joining ...
متن کاملHierarchical tree clustering of fuzzy number
This paper presents a new hierarchical tree approach to clustering fuzzy data, namely extensional tree (ET) clustering algorithm. It defines a dendrogram over fuzzy data and using a new distance between fuzzy numbers based on -cuts. The present work is based on hierarchical clustering algorithm unlike existing methods which improve FCM to support fuzzy data. The Proposed ET clustering algorithm...
متن کاملApproximate Hierarchical Clustering via Sparsest Cut and Spreading Metrics
Dasgupta recently introduced a cost function for the hierarchical clustering of a set of points given pairwise similarities between them. He showed that this function is NP -hard to optimize, but a top-down recursive partitioning heuristic based on an αn-approximation algorithm for uniform sparsest cut gives an approximation of O(αn logn) (the current best algorithm has αn = O( √ log n)). We sh...
متن کاملHierarchical Overlapping Clustering of Network Data Using Cut Metrics
A novel method to obtain hierarchical and overlapping clusters from network data – i.e., a set of nodes endowed with pairwise dissimilarities – is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings of the node set depending on the resolution or degree of similarity desired, and it is overlapping since it allows nodes to belong to more ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Computing
سال: 2011
ISSN: 0097-5397,1095-7111
DOI: 10.1137/100806886