## 1 Introduction
Various algorithms examine identical objects and properties whilst seeking a different number of clusters. Such an approach is advantageous for data sets with an inherently multi-scale structure, such as satellite image data. However, collaborative clustering faces many challenges, including which collaborating algorithms to choose in the collaboration process, the information that is exchanged, on what basis the collaborators make this decision and when to stop the collaboration, as increasing or decreasing the collaborative process leads to negative collaboration. Thus, in this work, ensemble learning is utilised to create several clusterings and to combine them in order to obtain a final consensus clustering. This technique promotes classification accuracy and prediction performance. A group of individual models is referred to as an ensemble model [7]. Typically, alternative methods, variable algorithm parameters or random data sampling create the initial clustering. Ensemble methods are simple supervised learning methods dependent on consensus-based techniques, as defining a set of predictive functions in order to produce an aggregated prediction function is straightforward. For example, a linear combination is used in boosting. It is also easy to gauge the effectiveness of individual predictive functions as well as the variety of the group of task contenders for inclusion in the final combined global decision function. The main issues in the current research are that all the work in the literature used supervised learning algorithms for the prediction of heart disease; there is no work that utilised unsupervised learning algorithms or collaborative clustering technology for this application. In this paper, a hybrid model is proposed for unsupervised learning techniques. A summary of this contribution is detailed below:  
* The presented model comprises two main components: collaborative clustering and combining decisions (ensemble approach). * The problem of mixed data clustering, i.e., quantitative and qualitative, here deemed multi-view clustering, whereby each offer is processed using specialised clustering algorithms, was faced. Since each algorithm operates on a different area of the data set features, the proposed collaborative clustering framework is horizontal collaborative. * A clustering strategy similar to the stacking approach was employed, together with a logistic regression model as a meta-classifier. The latter was trained on the results of expert model predictions and then employed in order to predict the final decision. * The efficacy of the approach with respect to different clustering algorithm optimisation and meta-classifier results was proven using the collaborative technique. The strength of this proposed collaborative method is that it does not require the prototypes or models used by the different algorithms to be shared during the collaborative step. Only the solution vectors produced by all the algorithms require sharing. * Controlling collaborative step continuity also avoids negative collaboration. The remainder of this paper is divided into several sections. Section 2 includes relevant work on predicting heart disease using supervised and unsupervised learning, together with a description of some general studies related to collaborative clustering. In Section 3, the proposal for collaborative clustering and the ensemble approach is presented; each stage of the methodology is explained in detail. Section 4 contains the results and discussion relating to a number of experiments for both parallel and reinforcement scenarios. The article concludes with a conclusion and thoughts relating to future work.