What are the basic requirements of clustering?

What are the basic requirements of clustering?

The main requirements that a clustering algorithm should satisfy are:

• scalability;
• dealing with different types of attributes;
• discovering clusters with arbitrary shape;
• minimal requirements for domain knowledge to determine input parameters;
• ability to deal with noise and outliers;

What is a protein cluster?

Introduction. Protein Clusters is a database of proteins grouped together by sequence similarity – clusters. Clustering is a well-known method in statistics and computer science. For a given set of entities clusters are defined as subsets that are homogeneous and well separated.

Is it possible to cluster hundreds of millions of sequences?

However, clustering hundreds of millions of sequences is impractical using current algorithms because their runtimes scale as the input set size N times the number of clusters K, which is typically of similar order as N, resulting in runtimes that increase almost quadratically with N.

How to cluster protein sequences in linear time?

In protein sequence clustering K is typically of similar size to N and therefore the total runtime scales almost quadratically with N. The fast sequence prefilters speed up each pairwise comparison by a large factor 1/ pmatch but cannot improve the time complexity of O ( NK ).

Is there an algorithm for clustering peptide sequences?

Currently, clustering methods rely on a prealignment as supplementary information to guide the construction of clusters. This chapter introduces a novel algorithm to cluster nucleotide and peptide sequences. The algorithm is a no-reference approach that utilizes only the sequences as input.

Which is the best algorithm for clustering data?

We developed Linclust, the first clustering algorithm whose runtime scales as N, independent of K. It can also cluster datasets several times larger than the available main memory. We cluster 1.6 billion metagenomic sequence fragments in 10 h on a single server to 50% sequence identity, >1000 times faster than has been possible before.