Enhancing an evolving tree-based text document visualization model with fuzzy c-Means clustering

An improved evolving model, i.e., Evolving Tree (ETree) with Fuzzy c-Means (FCM), is proposed for undertaking text document visualization problems in this study. ETree forms a hierarchical tree structure in which nodes (i.e., trunks) are allowed to grow and split into child nodes (i.e., leaves), and each node represents a cluster of documents. However, ETree adopts a relatively simple approach to split its nodes. Thus, FCM is adopted as an alternative to perform node splitting in ETree. An experimental study using articles from a flagship conference of Universiti Malaysia Sarawak (UNIMAS), i.e., Engineering Conference (ENCON), is conducted. The experimental results are analyzed and discussed, and the outcome shows that the proposed ETree-FCM model is effective for undertaking text document clustering and visualization problems.

Investigation of a prefetch model for low bandwidth networks

We investigate speculative prefetching under a model in which prefetching is neither aborted nor preempted by demand fetch but instead gets equal priority in network bandwidth utilisation. We argue that the non-abortive assumption is appropriate for wireless networks where bandwidth is low and latency is high, and the non-preemptive assumption is appropriate for Internet where prioritization is not always possible. This paper assumes the existence of an access model to provide some knowledge about future accesses and investigates analytically the performance of a prefetcher that utilises this knowledge. In mobile computing, because resources are severely constrained, performance prediction is as important as access prediction. For uniform retrieval time, we derive a theoretical limit of improvement in access time due to prefetching. This leads to the formulation of an optimal algorithrn for prefetching one access ahead. For non-uniform retrieval time, two different types of prefetching of multiple documents, namely mainline and branch prefetch, are evaluated against prefetch of single document. In mainline prefetch, the most probable sequence of future accesses is prefetched. In branch prefetch, a set of different alternatives for future accesses is prefetched. Under some conditions, mainline prefetch may give slight improvement in user-perceived access time over single prefetch with nominal extra retrieval cost, where retrieval cost is defined as the expected network time wasted in non-useful prefetch. Branch prefetch performs better than mainline prefetch but incurs more retrieval cost.

Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals.

Exemplifying a model-eliciting task for primary school pupils

Mathematical modelling is a field that is gaining prominence recently in mathematics educaiton research and has generated interests in schools as well.  In Singapore, modelling and applications are included as process componens in revised 2007 curriculum document (MOE, 2007) as keeping to reform efforst. In Indonesia, efforts to place stronger emphasis on connecting school mathematics with real-world contexts and applications have started in Indonesian primary schools with the Pendidikan Matematika Realistik Indonesia (PMRI) movement a decade ago (Sembiring, Hoogland, Dolk, 2010). Amidst others, modeling activities are gradually introduced in Singapore and Indonesian schools to demonstrte the relevance of school mathematics with real-world problems. However, on order for it to find a place in the mathematics classroom, ther eis a need for teacher-practitioners to know what mathematical modelling and what a modelling task is. This paper sets out to exemplify a model-eliciting task that has been designed and used in both a Singapore and Indonesian mathematics classroom. Mathematical modelling, the features of a model-eliciting task, and its potential and advice on implementation are discussed. 

A sequence based dynamic SOM model for text clustering

Text clustering can be considered as a four step process consisting of feature extraction, text representation, document clustering and cluster interpretation. Most text clustering models consider text as an unordered collection of words. However the semantics of text would be better captured if word sequences are taken into account.

In this paper we propose a sequence based text clustering model where four novel sequence based components are introduced in each of the four steps in the text clustering process.

Experiments conducted on the Reuters dataset and Sydney Morning Herald (SMH) news archives demonstrate the advantage of the proposed sequence based model, in terms of capturing context with semantics, accuracy and speed, compared to clustering of documents based on single words and n-gram based models.

Graph-induced restricted Boltzmann machines for document modeling

Discovering knowledge from unstructured texts is a central theme in data mining and machine learning. We focus on fast discovery of thematic structures from a corpus. Our approach is based on a versatile probabilistic formulation – the restricted Boltzmann machine (RBM) –where the underlying graphical model is an undirected bipartite graph. Inference is efficient document representation can be computed with a single matrix projection, making RBMs suitable for massive text corpora available today. Standard RBMs, however, operate on bag-of-words assumption, ignoring the inherent underlying relational structures among words. This results in less coherent word thematic grouping. We introduce graph-based regularization schemes that exploit the linguistic structures, which in turn can be constructed from either corpus statistics or domain knowledge. We demonstrate that the proposed technique improves the group coherence, facilitates visualization, provides means for estimation of intrinsic dimensionality, reduces overfitting, and possibly leads to better classification accuracy.

Impact of a modified nursing handover model for improving nursing care and documentation in the emergency department: A pre- and post-implementation study

The aim of this study was to evaluate whether implementation of a new nursing handover model led to improved completion of nursing care activities and documentation. A pre- and post-implementation study, using a survey and document audit, was conducted in a hospital ED in Melbourne. A convenience sample of nurses completed the survey at baseline (n = 67) and post-intervention (n = 59), and the audit was completed at both time points. Results showed significant improvements in several processes: handover in front of the patient (P < 0.001), patients contributed and/or listened to handover discussions (P < 0.001), and provision of adequate information about all patients in the department (P < 0.001). Nurses also reported a reduction in omission of vital signs (P = 0.022) during handover. Three hundred sixty-eight medical records were audited in the two study periods: 173 (pre-intervention) and 195 (post-intervention). Statistically significant improvements in the completion of two nursing care tasks and three documentation items were identified. The findings suggest that implementation of a new handover model improved completion of nursing care activities and documentation, and transfer of important information to nurses on oncoming shifts.