An Efficient Learning of Constraints For Semi-Supervised Clustering using Neighbour Clustering Algorithm

Data mining is the process of finding the previously unknown and potentially interesting patterns and relation in database. Data mining is the step in the knowledge discovery in database process (KDD) .The structures that are the outcome of the data mining process must meet certain condition so that these can be considered as knowledge. These conditions are validity, understandability, utility, novelty, interestingness. Researcher identifies two fundamental goals of data mining: prediction and description. The proposed research work suggests the semi-supervised clustering problem where to know (with varying degree of certainty) that some sample pairs are (or are not) in the same class. A probabilistic model for semi-supervised clustering based on Shared Semi-supervised Neighbor clustering (SSNC) that provides a principled framework for incorporating supervision into prototype-based clustering. Semi-supervised clustering that combines the constraint-based and fitness-based approaches in a unified model. The proposed method first divides the Constraint-sensitive assignment of instances to clusters, where points are assigned to clusters so that the overall distortion of the points from the cluster centroids is minimized, while a minimum number of must-link and cannot-link constraints are violated. Experimental results across UCL Machine learning semi-supervised dataset results show that the proposed method has higher F-Measures than many existing Semi-Supervised Clustering methods

Constrained Distance Based Clustering for Satellite Image Time-Series

International audienceThe advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and k-Means based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semi-supervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multi-spectral Landsat images non-uniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images non-uniformly sampled between 2016 and 2018

Efficient incremental constrained clustering

Clustering with constraints is an emerging area of data min-ing research. However, most work assumes that the con-straints are given as one large batch. In this paper we ex-plore the situation where the constraints are incrementally given. In this way the user after seeing a clustering can provide positive and negative feedback via constraints to critique a clustering solution. We consider the problem of efficiently updating a clustering to satisfy the new and old constraints rather than re-clustering the entire data set. We show that the problem of incremental clustering under con-straints is NP-hard in general, but identify several sufficient conditions which lead to efficiently solvable versions. These translate into a set of rules on the types of constraints that can be added and constraint set properties that must be maintained. We demonstrate that this approach is more ef-ficient than re-clustering the entire data set and has several other advantages

Model selection for semi-supervised clustering

Although there is a large and growing literature that tackles the semi-supervised clustering problem (i.e., using some labeled objects or cluster-guiding constraints like \must-link" or \cannot-link"), the evaluation of semi-supervised clustering approaches has rarely been discussed. The application of cross-validation techniques, for example, is far from straightforward in the semi-supervised setting, yet the problems associated with evaluation have yet to be addressed. Here we\ud summarize these problems and provide a solution.\ud Furthermore, in order to demonstrate practical applicability of semi-supervised clustering methods, we provide a method for model selection in semi-supervised clustering based on this sound evaluation procedure. Our method allows the user to select, based on the available information\ud (labels or constraints), the most appropriate clustering model (e.g., number of clusters, density-parameters) for a given problem.NSERC (Canada)FAPESP (Brazil)CNPq (Brazil