The aim of this paper is to present a novel subspace clustering method named FINDIT. Clustering is the process of finding interesting patterns residing in the dataset by grouping similar data objects from dissimilar ones based on their dimensional values. Subspace clustering is a new area of clustering which achieves the clustering goal in high dimension by allowing clusters to be formed with t...

Clustering techniques have been used on educational data to find groups of students who demonstrate similar learning patterns. Many educational data are relatively small in the sense that they contain less than a thousand student records. At the same time, each student may participate in dozens of activities, and this means that these datasets are high dimensional. Finding meaningful clusters f...

While subspace clustering emerged as an application of pattern mining and some of its early advances have probably been inspired by developments in pattern mining, over the years both fields progressed rather independently. In this paper, we identify a number of recent developments in pattern mining that are likely to be applicable to alleviate or solve current problems in subspace clustering a...

Data mining is a process of discovering and exploiting hidden patterns from data. Clustering as an important task of data mining divides the observations into groups (clusters), which is according to the principle that the observations in the same cluster are similar, and the ones from different clusters are dissimilar to each other. Subspace clustering enables clustering in subspaces within a ...

Reconstruction based subspace clustering methods compute a self reconstruction matrix over the samples and use it for spectral clustering to obtain the final clustering result. Their success largely relies on the assumption that the underlying subspaces are independent, which, however, does not always hold in the applications with increasing number of subspaces. In this paper, we propose a nove...