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Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cmdc.200800408openalex-snapshot
Improving the Search Performance of Extended Connectivity Fingerprints through Activity‐Oriented Feature Filtering and Application of a Bit‐Density‐Dependent Similarity Function
inferredcites →https://doi.org/10.1002/jcc.540070419openalex-snapshot
Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity
inferredcites →https://doi.org/10.1002/minf.201200110openalex-snapshot
Graph‐Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures
inferredcites →https://doi.org/10.1007/978-3-642-23318-0_2openalex-snapshot
What Is Quantum Information Retrieval?
inferredcites →https://doi.org/10.1007/s10822-012-9543-4openalex-snapshot
Ligand expansion in ligand-based virtual screening using relevance feedback
inferredcites →https://doi.org/10.1016/0223-5234(87)90020-1openalex-snapshot
Similarity and clustering in chemical information systems
inferredcites →https://doi.org/10.1016/j.bmc.2012.03.010openalex-snapshot
Combining multiple classifications of chemical structures using consensus clustering
inferredcites →https://doi.org/10.1016/j.jmgm.2009.06.006openalex-snapshot
Clustering files of chemical structures using the Székely–Rizzo generalization of Ward's method
inferredcites →https://doi.org/10.1016/j.patcog.2009.11.012openalex-snapshot
On voting-based consensus of cluster ensembles
inferredcites →https://doi.org/10.1021/ci00010a010openalex-snapshot
Clustering of chemical structures on the basis of two-dimensional similarity measures
inferredcites →https://doi.org/10.1021/ci00021a011openalex-snapshot
Similarity Searching and Clustering of Chemical-Structure Databases Using Molecular Property Data
inferredcites →https://doi.org/10.1021/ci0342674openalex-snapshot
Clustering Files of Chemical Structures Using the Fuzzy k -Means Clustering Method
inferredcites →https://doi.org/10.1021/ci100090popenalex-snapshot
Ligand-Based Virtual Screening Using Bayesian Networks
inferredcites →https://doi.org/10.1021/ci100232hopenalex-snapshot
New Fragment Weighting Scheme for the Bayesian Inference Network in Ligand-Based Virtual Screening
inferredcites →https://doi.org/10.1021/ci8002649openalex-snapshot
Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data
inferredcites →https://doi.org/10.1021/ci9501047openalex-snapshot
Use of Structure−Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection
inferredcites →https://doi.org/10.1080/01621459.1971.10482356openalex-snapshot
Objective Criteria for the Evaluation of Clustering Methods
inferredcites →https://doi.org/10.1080/01621459.1983.10478008openalex-snapshot
A Method for Comparing Two Hierarchical Clusterings
inferredcites →https://doi.org/10.1080/14756360802204748openalex-snapshot
3D Pharmacophore, hierarchical methods, and 5-HT4receptor binding data
inferredcites →https://doi.org/10.1093/bioinformatics/btg038openalex-snapshot
Bagging to improve the accuracy of a clustering procedure
inferredcites →https://doi.org/10.1109/tpami.2003.1240115openalex-snapshot
Bagging for path-based clustering
inferredcites →https://doi.org/10.1109/tpami.2007.1138openalex-snapshot
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
inferredcites →https://doi.org/10.1137/s1064827595287997openalex-snapshot
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
inferredcites →https://doi.org/10.1142/s0218001411008683openalex-snapshot
A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS
inferredcites →https://doi.org/10.1145/266021.266273openalex-snapshot
Multilevel hypergraph partitioning
inferredcites →https://doi.org/10.1162/153244303321897735openalex-snapshot
10.1162/153244303321897735
inferredcites →https://doi.org/10.1177/1087057105281365openalex-snapshot
Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up
inferredcites →https://doi.org/10.1186/1758-2946-4-37openalex-snapshot
Voting-based consensus clustering for combining multiple clusterings of chemical structures
inferredcites →https://doi.org/10.2307/3498751openalex-snapshot
Nonparametric Statistics for the Behavioral Sciences
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-1-4939-0808-0_26openalex-snapshot
A Review on Consensus Clustering Methods
inferred← citeshttps://doi.org/10.1007/s10822-014-9750-2openalex-snapshot
Weighted voting-based consensus clustering for chemical structure databases
inferred← citeshttps://doi.org/10.1007/s10822-022-00488-9openalex-snapshot
Examining unsupervised ensemble learning using spectroscopy data of organic compounds
inferred← citeshttps://doi.org/10.1016/j.cosrev.2018.01.003openalex-snapshot
Cluster ensembles: A survey of approaches with recent extensions and applications
inferred← citeshttps://doi.org/10.1021/ci300442uopenalex-snapshot
Consensus Methods for Combining Multiple Clusterings of Chemical Structures
inferred← citeshttps://doi.org/10.1109/access.2020.2973216openalex-snapshot
Clustering Mixed Numeric and Categorical Data With Cuckoo Search
inferred← citeshttps://doi.org/10.1109/access.2021.3057113openalex-snapshot
A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data
inferred← citeshttps://doi.org/10.1109/access.2023.3279489openalex-snapshot
Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering
inferred← citeshttps://doi.org/10.1109/ic2ecs60824.2023.10493712openalex-snapshot
Research on Power System Load Clustering based on Signal Analysis and K-Prototypes
inferred← citeshttps://doi.org/10.1142/s021800141550024xopenalex-snapshot
An Initialization Method for Clustering Mixed Numeric and Categorical Data Based on the Density and Distance
inferred← citeshttps://doi.org/10.12785/amis/090621openalex-snapshot
A novel cluster center initialization method for the k-prototypes algorithms using centrality and distance
inferred← citeshttps://doi.org/10.1371/journal.pone.0127125openalex-snapshot
A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data
inferred← citeshttps://doi.org/10.1371/journal.pone.0162259openalex-snapshot
What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm
inferred← citeshttps://doi.org/10.3233/jifs-18146openalex-snapshot
Clustering mixed numeric and categorical data with artificial bee colony strategy
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