Nizar Bouguila, On multivariate binary data clustering and feature weighting, Computational Statistics Data Analysis, v.54 n.1, p.120-134, January, 2010 [doi>10.1016/j.csda.2009.07.013]Bouguila, N. (2010). On m
This article focuses on mining the block of one's or zero's (constant co-cluster) in the binary data. It represents the likelihood characteristics among the local group of elements in the data. For this purpose a score based co-clustering approach is proposed in this article. Initially this...
TheeLassonetwork for these data is given inFigure 3. To analyse the depression network, we focus on the most prominent properties of nodes in a network: node strength, betweenness and clustering coefficient (Figure 4). Node strength is a measure of the number of connections a node has, weigh...
-tc <str>, Path to the true clusters assignments to compare clustering methods. -td <str>, Path to the true/raw data/genotypes. Model Arguments -FN <float>, Replace <float> with the fixed error rate for false negatives. -FP <float>, Replace <float> with the fixed error rate for fal...
Direct encoding: the work [50] designed a vector to represent five important variables for oversampling, i.e., the number of data to generate, number of clusters, the number of nearest neighbors with each cluster for oversampling, the clustering method, and oversampling method. Indirect encodin...
Note that classic classification methods used in machine learning (for instance clustering algorithms) compute and assign clusters/groups to incoming data. Our goal is merely to compute an explicit justification of the data. Our problem is also different from frequent itemsets computation in the contex...
Useful for building dendrograms and Hierarchical clustering, for example (see also Cluster analysis). Example: Comparator comparator = Comparator.create(core); int distance = comparator.distance(ACKMessageHex.class, ACKMessageHexByteChecksum.class); double similarity = comparator.similarity(ACKMessageHex....
Binary Partitioning refers to the process of dividing data or nodes into hardware and software components based on specific criteria such as feasibility and cost, often at an instruction level of granularity. AI generated definition based on: Readings in Hardware/Software Co-Design, 2002 About this...
We consider the unsupervised learning problem of assigning labels to unlabeled data. A naive approach is to use clustering methods, but this works well only when data is properly clustered and each cluster corresponds to an underlying class. In this paper, we first show that this unsupervised ...
Bayesian non-parametric clustering (BnpC) of binary data with missing values and uneven error rates - cbg-ethz/BnpC