Semi-Supervised Clustering and Feature Discrimination with Instance-Level Constraints

Model Based Unsupervised Learning Guided by Abundant Background Samples

“…The first term in (12) results in an addition of a value in the range of [0, αMThreshold] depending on the relative distance between the clusters pairs. In other words, the merge is penalized depending on the distance to a maximum of αMThreshold, which is a fraction of the merge threshold itself.…”

“…The normalized distance and the resulting error ratio of the merge are used to measure the cost of the merge as: (12) In (12) α is a scaling factor whose value is in the range [0, 1]. A value of α close to one results a merge that is only based on the least distance while α close to zero results in a merge based on the least resulting error ratio.…”

“…Many solutions have been already proposed [5]- [8] where most of them begin with a set of initial clusters followed by split, merge or cancelation operations until some acceptable state of clusters is reached. Other methods require user feedback to correct the clustering in a semi-supervised process [9]- [12].…”

Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento

“…Em (Frigui e Nasraoui, 2004) surge a terceira versão do algoritmo SCAD, dessa vez com uma nova função-custo na qual o segundo termo da Expressão (2.14) não está presente . Essa versão serviu de base para a criação de outros algoritmos, como um algoritmo de agrupamento semi-supervisionado (Frigui e Mahdi, 2007) e um algoritmo de agrupamento relacional . Daqui em diante o termo SCAD irá se referir à essa última versão, i.e., à versão proposta em (Frigui e Nasraoui, 2004), definida adiante nesta mesma seção.…”

Image annotation and retrieval based on multi-modal feature clustering and similarity propagation.

“…Clustering and Attribute Discrimination (sSCAD) [73] algorithms. These algorithms have been applied successfully to categorize large collections of images or image regions.…”