Our research employs data mining methods to extract this design information and improve its accessibility to design engineers. This paper focuses on one aspect of the overall research agenda, clustering BOMs into families and subfamilies. It extends previous work on a graph-based similarity measure ...
Clustering is a basic problem that arises in many different areas, such as data mining, machine learning, pattern recognition, and image classification (Xu and Wunsch, 2005; Filippone et al., 2008). The aim is to group similar data points into the same cluster, and there is also no need...
Traditional graph mining challenges include frequent sub-graph mining, graph matching, graph classification, graph clustering, etc. Although with deep learning, some downstream tasks can be directly solved without graph mining as an intermediate step, the basic challenges are worth being studied in the...
Methods for clustering can be divided into hierarchical, graph-based (equivalent to the graph partitioning), model-based or mixed methods [15, 16]. An additional stage in the representation of the records with their similarities may be necessary. From a representation of records in clusters, we...
Damian D, Orešič M, Verheij E, Meulman J, Friedman J, Adourian A, Morel N, Smilde A, Greef J (2007) Applications of a new subspace clustering algorithm (COSA) in medical systems biology. Metabolomics 3: 69–77 Article Google Scholar Duff IS, Koster J (2001) On algorithms for ...
In Section 2, we review existing me- thods for comparing the similarity of two words, and select the most suitable for our need. The new similarity measure is then introduced in Section 2. It is applied to ag- glomerative clustering in Section 3 with real data and compared against ...
(a), or by their respective spatial location (‘layer’, according to the zonation analysis done in15) (b).c,d, Heatmaps of the expression ofPck1(c) andOat(d) following scPrisma analysis as a function of the sampling time and zonation layer.e, ARI ofKMeans clustering withK =...
(2007) Trajectory clustering: A partition-and-group framework. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, Beijing, 12-14 June 2007, 593-604. [5] Giannotti, F., Nanni, M. and Pedreschi, D. (2006) Efficient mining of sequences with temporal ...
This is applied in clustering algorithms, text analysis, and in measuring the similarity of sets, such as in plagiarism detection. 7. Metaphone 3 This is a phonetic algorithm for indexing words by their sound when pronounced in English, designed to account for variations in spelling and pronuncia...
“type,” and more recent advances include: use in semisupervised learning, spectral clustering, and seed-selection processes (see [18], [19], [20] respectively), exploration of other methods for using seeds in graph matching (see [21], [22]), along with a few surveys covering seeded ...