Text mining with constraint tensor decomposition
Text mining, as a special case of data mining, refers to the estimation of knowledge or parameters necessary for certain purposes, such as unsupervised clustering by observing various documents. In this context, the topic of a docu- ment can be seen as a hidden variable, and words are multi-view variables related to each other by a topic. The main goal in this paper is to estimate the probability of topics, and conditional probability of words given topics. To this end, we use non negative Canonical Polyadic (CP) decomposition of a third order moment tensor of observed words.