2021 IEEE International Conference on Big Knowledge (ICBK) 2021
DOI: 10.1109/ickg52313.2021.00070
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A survey on Optimisation-based Semi-supervised Clustering Methods

Abstract: Clustering methods are developed for categorizing data points into different groups so that data points within each group have high similarities. Classic clustering algorithms are unsupervised, meaning that there is not any kind of complementary information to be utilized for attaining better clustering results. However, in some clustering problems, one may have supplementary information which can be employed for guiding the clustering process. In the presence of such information, the problem is semi-supervise… Show more

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Cited by 7 publications
(3 citation statements)
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“…As online data have evolved, so have clustering algorithms [10,11,15,17]. Both unsupervised and supervised clustering methods dot the literature [19,21,29]. In particular, a recent paper [24] performs temporal predictive clustering on dynamic user data.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…As online data have evolved, so have clustering algorithms [10,11,15,17]. Both unsupervised and supervised clustering methods dot the literature [19,21,29]. In particular, a recent paper [24] performs temporal predictive clustering on dynamic user data.…”
Section: Related Workmentioning
confidence: 99%
“…In media parlance [3], when a message is sent to a segment, Reach occurs provided both match and exposure are realized. Yet, even advanced algorithms for discovery, unsupervised or supervised, ignore these uncertainties of reach inherent in delivery [10,11,15,17,19,21,29]. Instead, those focus only on performance for discovery while tacitly assuming away these delivery roadblocks.…”
Section: Introductionmentioning
confidence: 99%
“…However, this formulation does not allow for exceptions: all the constraints must be satisfied. Such a requirement is, in general, not the best choice [38], and in our case, it may further complicate the search for an admissible consensus partition because the input partitions do not necessarily satisfy the given constraints. It seems more convenient to use soft constraints, where compliance is rewarded but violation is still allowed.…”
Section: Constrained Consensus Clustering As An Optimization Problemmentioning
confidence: 99%