A Novel method for Frequent Pattern Mining
... summaries are produced at diverse levels of granularity, according to the concept hierarchies. Mining large datasets became a major issue. Hence research focus was diverted to solve this issue in all respect. It was the primary requirement to devise fast algorithms for finding frequent item sets as ...
... summaries are produced at diverse levels of granularity, according to the concept hierarchies. Mining large datasets became a major issue. Hence research focus was diverted to solve this issue in all respect. It was the primary requirement to devise fast algorithms for finding frequent item sets as ...
Optimization of Association Rule Learning in Distributed Database
... the data structure that represents candidate set c, which is initially assumed to be zero. The most important part of the implementation is the data structure used for storing the candidate sets, and counting their frequencies. L1 = {frequent items} for (k=2; Lk-1=Ø; k++) do begin Ck = candidates ge ...
... the data structure that represents candidate set c, which is initially assumed to be zero. The most important part of the implementation is the data structure used for storing the candidate sets, and counting their frequencies. L1 = {frequent items} for (k=2; Lk-1=Ø; k++) do begin Ck = candidates ge ...
Subspace Clustering for High Dimensional Categorical
... This difficulty that conventional clustering algorithms encounter in dealing with high dimensional data sets motivates the concept of subspace clustering or projected clustering[3] whose goal is to find clusters embedded in subspaces of the original data space with their own associated dimensions. A ...
... This difficulty that conventional clustering algorithms encounter in dealing with high dimensional data sets motivates the concept of subspace clustering or projected clustering[3] whose goal is to find clusters embedded in subspaces of the original data space with their own associated dimensions. A ...
AN EFFICIENT HILBERT CURVE
... data points in a d-dimensional metric space, partition the data points into k clusters such that the data points within a cluster are more similar to each other than data points in di erent clusters. Cluster analysis has been widely applied to many areas such as medicine, social studies, bioinformat ...
... data points in a d-dimensional metric space, partition the data points into k clusters such that the data points within a cluster are more similar to each other than data points in di erent clusters. Cluster analysis has been widely applied to many areas such as medicine, social studies, bioinformat ...
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 ...
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.