Contact
How to shop
Delivery
Shopping guide
Transitivity Clustering
Clustering biological data by unraveling hidden transitive substructures
English
151 b
Up to 30 days for returns
Customers also purchased
/
Adobe ePub DRM
4.34
€
Clustering is a computational technique for the assignment of objects into groups of similar elements. Generally, it is widely used for business data interpretation, natural language analyses, and image processing. Typical bioinformatic applications are the detection of homologous proteins and the identification of co-expressed genes. Here, we introduce Transitivity Clustering and its accompanying software framework TransClust, a homogeneous data partitioning method based on Weighted Transitive Graph Projection. It aims for unraveling hidden transitive substructures in a given similarity graph deduced from a pairwise similarity measure. Transitivity Clustering is an efficient technique that is capable of processing hundreds of thousands of data points while still being robust against outliers and noise. A single, intuitive density parameter determines the number and the size of the clusters; with provable attributes. In addition, we present extensions of the underlying graph model in order to create hierarchies and overlaps, as well as comparisons against alternative clustering approaches and real-world application cases.
About the book
English
Give this book today
It's easy
1 Add to cart and choose Deliver as present at the checkout 2 We'll send you a voucher 3 The book will arrive at the recipient's addressYou might also be interested in
/
Paperback
26.87
€
/
Paperback
79.72
€
/
Stationery items
54.86
€
/
Hardback
308.10
€
