Integrating instance-level and attribute-level knowledge into document clustering

In this paper, we present a document clustering framework incorporating instance-level knowledge in the form of pairwise constraints and attribute-level knowledge in the form of keyphrases. Firstly, we initialize weights based on metric learning with pairwise constraints, then simultaneously learn two kinds of knowledge by combining the distance-based and the constraint-based approaches, finally evaluate and select clustering result based on the degree of users’ satisfaction. The experimental results demonstrate the effectiveness and potential of the proposed method.

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.

Document clustering in correlation similarity measure space

This paper presents a new spectral clustering method called correlation preserving indexing (CPI), which is performed in the correlation similarity measure space. In this framework, the documents are projected into a low-dimensional semantic space in which the correlations between the documents in the local patches are maximized while the correlations between the documents outside these patches are minimized simultaneously. Since the intrinsic geometrical structure of the document space is often embedded in the similarities between the documents, correlation as a similarity measure is more suitable for detecting the intrinsic geometrical structure of the document space than euclidean distance. Consequently, the proposed CPI method can effectively discover the intrinsic structures embedded in high-dimensional document space. The effectiveness of the new method is demonstrated by extensive experiments conducted on various data sets and by comparison with existing document clustering methods.

Sparse subspace representation for spectral document clustering

We present a novel method for document clustering using sparse representation of documents in conjunction with spectral clustering. An ℓ1-norm optimization formulation is posed to learn the sparse representation of each document, allowing us to characterize the affinity between documents by considering the overall information instead of traditional pair wise similarities. This document affinity is encoded through a graph on which spectral clustering is performed. The decomposition into multiple subspaces allows documents to be part of a sub-group that shares a smaller set of similar vocabulary, thus allowing for cleaner clusters. Extensive experimental evaluations on two real-world datasets from Reuters-21578 and 20Newsgroup corpora show that our proposed method consistently outperforms state-of-the-art algorithms. Significantly, the performance improvement over other methods is prominent for this datasets.

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.

Pauline Kael and the Western genre as critical displacement of self & nation: metaphorics and affects of 'taste' in American film criticism

Pauline Kael (1919–2001) is one of the most influential American film critics of the second half of the twentieth century. Many people are writing on her presently, with at least half an eye to her future cultural, political and historical importance. Certainly the full impact of Kael’s work, both within and beyond the borders of cinema (however defined), has not yet been established. This article unpacks the mechanisms and operations of ‘taste’ in Kael’s writings by using two notions drawn from Roland Barthes’ observations about another key figure of current cultural critique: Julia Kristeva. The comparison of Kael with Kristeva is not dwelt upon; instead, the article focuses on how Kael used the concepts of ‘taste’ and ‘dis-taste’ to draw her readership into a field of what might be termed ‘permanent dissent’. This article concludes by sketching out why Jewish-American Kael’s taste might endure, through the dual transition she occupies from a Cold War to a post-Cold War period, and from an era when cinema was the supreme, undisputed, screen artform, to the rise of the myriad screen technologies of the networked, Internet age.

The poetic presentation of philosophy:Leo Strauss on Plato’s “symposium”

This essay undertakes a close analysis of Leo Strauss’s remarkable but undertreated Leo Strauss on Plato’s “Symposium,” reading it as opening a privileged purview of his own (and his students’) wider understandings of philosophy, poetry, and politics. The essay begins by drawing out Strauss’s three framing justifications for his manner of reading the Symposium as a document in the “ancient quarrel” of philosophy and poetry concerning which of the two should rightly shape the culture and ethical ideals of the Greeks (part 1). Then, following the course of Plato’s Symposium, the essay ascends through Strauss’s readings of the first five speeches in Plato’s dialogue (part 2) toward the highlight of Strauss’s reading, namely, his three remarkable sessions on Socrates’s speech. Part 3 analyses Strauss’s reading of this speech up to its climax, which Strauss argues involves the philosophical “demotion of poetry”: a criticism of poets as motivated by the Eros of fame and of tragic poetry as at its best creating captivating images of gods and heroes which reflect their creators’ self-love and patriotic love of “one’s own,”as against any transpolitical truth. Part 4 then looks at Strauss’s unusual reading of the culmination of Socrates’s great speech (Diotima on the “higher mysteries”) alongside Alkibiades’s speech in the Symposium as representing Plato’s “poetic presentation of philosophy.” The essay becomes more critical as it proceeds. Strauss’s reading of the Symposium, like his reading of the Republic, is remarkable for its own “demotion of metaphysics” in Plato, and in my concluding remarks, I will question this status, or disappearance, of metaphysics in Strauss’s Platonism and whether this disappearance compromises Strauss' ability to differentiate philosophy as he sees it from poetry.

Adult cardiothoracic transplant nursing: an ISHLT consensus document on the current adult nursing practice in heart and lung transplantation.

The role of nurses in cardiothoracic transplantation has evolved over the last 25 years. Transplant nurses work in a variety of roles in collaboration with multidisciplinary teams to manage complex pre- and post-transplantation issues. There is lack of clarity and consistency regarding required qualifications to practice transplant nursing, delineation of roles and adequate levels of staffing.