SCHAIN-IRAM: A Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information Networks. PROJECT TITLE : SCHAIN-IRAM: An Efficient and Effective Semi-Supervised Clustering Algorithm for Attributed Heterogeneous Information Networks ABSTRACT: A heterogeneous information network, also known as an HIN, is a network in which the nodes model objects of varying types, and the links model the relationships between the objects. Objects contained within an HIN will often have additional attributes associated with them so that the information they contain can be improved. An HIN that has attributes attached to it is referred to as an AHIN. We investigate the challenge of clustering objects within an AHIN by taking into account the similarities between the objects in terms of the values of their object attributes as well as the structural connectedness of the objects within the network. We demonstrate how the supervision signal, which is presented in the form of a must-link set and a cannot-link set, can be utilized to improve the clustering results that are obtained. We propose the SCHAIN algorithm as a solution to the clustering problem, along with two highly effective variants called SCHAIN-PI and SCHAIN-IRAM. These variants compute the eigenvectors of a matrix using the power iteration based method and the implicitly restarted Arnoldi method, respectively. Extensive experiments are carried out in which state-of-the-art clustering algorithms, including those based on SCHAIN, are compared with one another. According to the findings of our study, SCHAIN-IRAM outperforms its rivals in terms of clustering effectiveness and is significantly more efficient than its rivals. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Utilizing Multi-Objective Evolutionary Algorithm, mining High Quality Patterns RHINE is an acronym for Relation Structure-Aware Heterogeneous Information Network Embedding.