We consider clustering player behavior and learning the optimal team composition for multiplayer online games. The goal is to determine a set of descriptive play style groupings and learn a predictor for win/loss outcomes. The predictor takes in as input the play styles of the participants in each team; i.e., the various team compositions in a game. Our framework uses unsupervised learning to f...

The aim of this paper is to use unsupervised classification techniques in order to group the documents of a given huge collection into clusters. We approached this challenge by using a simple clustering algorithm (K-Star) in a recursive clustering process over subsets of the complete collection. The presented approach is a scalable algorithm which may automatically discover the number of cluste...

This paper presents a new technique for parallel and distributed unsupervised learning. It arises from a detailed analysis of the weaknesses of several, existing algorithms, the most important of which is the presence of intrinsically serial operations. The basic idea of this work, therefore, is the substitution of this latter with new operations, as similar as possible to the original ones, bu...

Clustering is one type of unsupervised learning where the goal is to partition the set of objects into groups called clusters. Faced to the difficulty to design a general purpose clustering algorithm and to choose a good, let alone perfect, set of criteria for clustering a data set, one solution is to resort to a variety of clustering procedures based on different techniques, parameters and/or ...

Discretization is the process of dividing a continuousvalued base attribute into discrete intervals, which highlight distinct patterns in the behavior of a related goal attribute. In this paper, we present an integrated visual framework in which several discretization strategies can be experimented with, and which visually assists the user in intuitively determining the appropriate number and l...

This paper discusses a model which conceptually demonstrates how infants could learn the normalization between infant-adult acoustics. The model proposes that the mapping can be inferred from the topological correspondences between the adult and infant acoustic spaces, that are clustered separately in an unsupervised manner. The model requires feedback from the adult in order to select the righ...

We describe a technique for attributing parts of a written text to a set of unknown authors. Nothing is assumed to be known a priori about the writing styles of potential authors. We use multiple independent clusterings of an input text to identify parts that are similar and dissimilar to one another. We describe algorithms necessary to combine the multiple clusterings into a meaningful output....

This paper proposes a new unsupervised technique for clustering a collection of documents written by distinct individuals into authorial components. We highlight the importance of utilizing syntactic structure to cluster documents by author, and demonstrate experimental results that show the method we outline performs on par with state-of-the-art techniques. Additionally, we argue that this fea...

This work is motivated by a question at the heart of unsupervised learning approaches: Assume we are collecting a number K of (subjective) opinions about some event E from K different agents. Can we infer E from them? Prima facie this seems impossible, since the agents may be lying. We model this task by letting the events be distributed according to some distribution p and the task is to estim...

We study functional activity in the human brain using functional magnetic resonance imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over 3 days of practice produces significant evidence of ‘learning’, in the sense...