When Pattern met Subspace Cluster — a Relationship Story
... Clearly, this is a rather naïve use of the concept of frequent itemsets in subspace clustering. What constitutes a good subspace clustering result is defined here apparently in close relationship to the design of the algorithm, i.e., the desired result appears to be defined according to the expected ...
... Clearly, this is a rather naïve use of the concept of frequent itemsets in subspace clustering. What constitutes a good subspace clustering result is defined here apparently in close relationship to the design of the algorithm, i.e., the desired result appears to be defined according to the expected ...
Mixture models and frequent sets
... We now move to the treatment of local patterns in large 0–1 datasets. Let R be a set of n observations over d variables, each observation either 0 or 1. For example, the variables can be the items sold in a supermarket, each observation corresponding to a basket of items bought by a customer. If man ...
... We now move to the treatment of local patterns in large 0–1 datasets. Let R be a set of n observations over d variables, each observation either 0 or 1. For example, the variables can be the items sold in a supermarket, each observation corresponding to a basket of items bought by a customer. If man ...
Modern Methods of Statistical Learning sf2935 Lecture 16
... should then represent groups of items (products, events, biological organisms) that have a lot in common. Creating clusters prior to application of some data analysis technique (decision trees, neural networks) might reduce the complexity of the problem by dividing the space of examples. These parti ...
... should then represent groups of items (products, events, biological organisms) that have a lot in common. Creating clusters prior to application of some data analysis technique (decision trees, neural networks) might reduce the complexity of the problem by dividing the space of examples. These parti ...
Scaling Clustering Algorithms to Large Databases
... CS varies depending on the density of points not compressed in the primary phase. Secondary datacompression has two fundamental parts: 1) locate candidate “dense” portions of the space not compressed in the primary phase, 2) applying a “tightness” (or “dense”) criterion to these candidates. Candidat ...
... CS varies depending on the density of points not compressed in the primary phase. Secondary datacompression has two fundamental parts: 1) locate candidate “dense” portions of the space not compressed in the primary phase, 2) applying a “tightness” (or “dense”) criterion to these candidates. Candidat ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.