Description Usage Arguments Details Value References Examples

The `EnsembleClustering`

includes the ensemble clustering methods CSPA, HGPA and MCLA which are graph-based consensus methods.

1 2 3 4 5 6 | ```
EnsembleClustering(List, type = c("data", "dist", "clust"),
distmeasure = c("tanimoto", "tanimoto"), normalize = c(FALSE, FALSE),
method = c(NULL, NULL), clust = "agnes", linkage = c("flexible",
"flexible"), alpha = 0.625, nrclusters = 7, gap = FALSE, maxK = 15,
ensembleMethod = c("CSPA", "HGPA", "MCLA", "Best"), waitingtime = 300,
file_number = 0, executable = FALSE)
``` |

`List` |
A list of data matrices. It is assumed the rows are corresponding with the objects. |

`type` |
indicates whether the provided matrices in "List" are either data matrices, distance matrices or clustering results obtained from the data. If type="dist" the calculation of the distance matrices is skipped and if type="clusters" the single source clustering is skipped. Type should be one of "data", "dist" or "clusters". |

`distmeasure` |
A vector of the distance measures to be used on each data matrix. Should be one of "tanimoto", "euclidean", "jaccard", "hamming". Defaults to c("tanimoto","tanimoto"). |

`normalize` |
Logical. Indicates whether to normalize the distance matrices or not, defaults to c(FALSE, FALSE) for two data sets. This is recommended if different distance types are used. More details on normalization in |

`method` |
A method of normalization. Should be one of "Quantile","Fisher-Yates", "standardize","Range" or any of the first letters of these names. Default is c(NULL,NULL) for two data sets. |

`clust` |
Choice of clustering function (character). Defaults to "agnes". |

`linkage` |
Choice of inter group dissimilarity (character) for each data set. Defaults to c("flexible,", "flexible") for two data sets. |

`alpha` |
The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625 and is only used if the linkage is set to "flexible" |

`nrclusters` |
The number of clusters to divide each individual dendrogram in. Default is c(7,7) for two data sets. |

`gap` |
Logical. Whether the optimal number of clusters should be determined with the gap statistic. Default is FALSE. |

`maxK` |
The maximal number of clusters to investigate in the gap statistic. Default is 15. |

`ensembleMethod` |
The method to be performed: "CSPA", "HGPA", "MCLA" or "Best". |

`waitingtime` |
The time in seconds to wait until the MATLAB results are generated. Defaults to 300. |

`file_number` |
The specific file number to be placed as a tag in the file generated by MATLAB. Defaults to 00. |

`executable` |
Logical. Whether the MATLAB functions are performed via an executable on the command line (TRUE, only possible for Linux systems) or by calling on MATLAB directly (FALSE). Defaults to FALSE. The files EnsembleClusteringC.m (CSPA), EnsembleClusteringH.m (HGPA), EnsembleClusteringM.m (MCLA) and MetisAlgorithm.m are present in the inst folder to be transformed in executables. |

Strehl2002IntClust introduce three heuristic algorithms to solve the cluster ensemble problem.
Each method starts by transforming the clustering solutions into a single hypergraph in which a hyperedge represents a single cluster.
The Cluster-based Similarity Partitioning Algorithm (CSPA) transforms the hypergraph into an overall similarity matrix which entries
represent the fraction of clusterings in which two objects are in the same cluster. The similarity matrix is considered as a
graph and the objects are reclustered with the graph partitioning algorithm METIS \insertCiteKarypis1998IntClust. Hyper-Graph Partitioning
Algorithm (HGPA) partitions the hypergraph directly by cutting a minimal number of hyperedges. It aims to obtain connected components of
approximately the same dimension. The partitioning algorithm is HMetis \insertCiteKarypis1997IntClust. The Meta-CLustering Algorithm (MCLA)
computes a similarity between the hyperedges (clusters) based on the Jaccard index. The resulting similarity matrix is used to build
a meta-graph which is partitioned by the METIS algorithm \insertRefKarypis1998IntClust into resulting meta-clusters.
The final partition of the objects is obtaining by appointing each object to the meta-cluster to which it is assigned the most. The `R`

code calls on the MATLAB code provided by \insertCiteStrehl2002IntClust. The MATLAB functions are included in the inst folder and should be located in the working directory. Shell script for the executable can be found in the inst folder as well.

The returned value is a list of two elements:

`DistM` |
A list with the distance matrix for each data structure |

`Clust` |
The resulting clustering |

The value has class 'Ensemble'.

Strehl2002IntClust \insertRefKarypis1997IntClust \insertRefKarypis1998IntClust

1 2 3 4 5 6 7 8 9 10 11 12 | ```
## Not run:
data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)
MCF7_CSPA=EnsembleClustering(List=L,type="data",distmeasure=c("tanimoto",
"tanimoto"),normalize=c(FALSE,FALSE),method=c(NULL,NULL),StopRange=FALSE,
clust="agnes",linkage=c("flexible","flexible"),nrclusters=c(7,7),gap=FALSE,
maxK=15,ensembleMethod="CSPA",executable=FALSE)
## End(Not run)
``` |

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