Document clustering with K-tree

This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.

The role of timber plantations in U.K. investment portfolios

Although timber plantations and forests are classified as forms of agricultural production, the ownership of this land classification is not limited to rural producers. Timber plantations and forests are now regarded as a long-term investment with both institutional and absentee owners. While the NCREIF property indices have been the benchmarks for the measurement of the performance of the commercial property market in the UK, for many years the IPD timberland index has recently emerged as the U.K. forest and timberland performance indicator. The IPD Forest index incorporates 126 properties over five regions in the U.K. This paper will utilise the IPD Forestry Index to examine the performance of U.K. timber plantations and forests over the period 1981-2004. In particular, issues to be critically assessed include plantation and forest performance analysis, comparative investment analysis, and the role of plantations and forests in investment portfolios, the risk reduction and portfolio benefits of plantations and forests in mixed-asset portfolios and the strategic investment significance of U.K. timberlands.

Random Indexing K-tree

Random Indexing K-tree is the combination of two algorithms suited for large scale document clustering.

Clustering with random indexing K-tree and XML structure

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

In-depth analysis of the J.A.C.K. model

The Texas Transportation Commission (“the Commission”) is responsible for planning and making policies for the location, construction, and maintenance of a comprehensive system of highways and public roads in Texas. In order for the Commission to carry out its legislative mandate, the Texas Constitution requires that most revenue generated by motor vehicle registration fees and motor fuel taxes be used for constructing and maintaining public roadways and other designated purposes. The Texas Department of Transportation (TxDOT) assists the Commission in executing state transportation policy. It is the responsibility of the legislature to appropriate money for TxDOT’s operation and maintenance expenses. All money authorized to be appropriated for TxDOT’s operations must come from the State Highway Fund (also known as Fund 6, Fund 006, or Fund 0006). The Commission can then use the balance in the fund to fulfill its responsibilities. However, the value of the revenue received in Fund 6 is not keeping pace with growing demand for transportation infrastructure in Texas. Additionally, diversion of revenue to nontransportation uses now exceeds $600 million per year. As shown in Figure 1.1, revenues and expenditures of the State Highway Fund per vehicle mile traveled (VMT) in Texas have remained almost flat since 1993. In the meantime, construction cost inflation has gone up more than 100%, effectively halving the value of expenditure.

Evaluation of TxDOT’s J.A.C.K. model for revenue and expenditure projections

This research report documents work conducted by the Center for Transportation (CTR) at The University of Texas at Austin in analyzing the Joint Analysis using the Combined Knowledge (J.A.C.K.) program. This program was developed by the Texas Department of Transportation (TxDOT) to make projections of revenues and expenditures. This research effort was to span from September 2008 to August 2009, but the bulk of the work was completed and presented by December 2008. J.A.C.K. was subsequently renamed TRENDS, but for consistency with the scope of work, the original name is used throughout this report.

Pyktree : a K-tree implementation in Python

In this paper we present pyktree, an implementation of the K-tree algorithm in the Python programming language. The K-tree algorithm provides highly balanced search trees for vector quantization that scales up to very large data sets. Pyktree is highly modular and well suited for rapid-prototyping of novel distance measures and centroid representations. It is easy to install and provides a python package for library use as well as command line tools.

PCR bias Toward the wild-type k-ras and p53 sequences: Implications for PCR detection of mutations and cancer diagnosis

PCR-based cancer diagnosis requires detection of rare mutations in k- ras, p53 or other genes. The assumption has been that mutant and wild-type sequences amplify with near equal efficiency, so that they are eventually present in proportions representative of the starting material. Work on factor IX suggests that this assumption is invalid for one case of near- sequence identity. To test the generality of this phenomenon and its relevance to cancer diagnosis, primers distant from point mutations in p53 and k-ras were used to amplify wild-type and mutant sequences from these genes. A substantial bias against PCR amplification of mutants was observed for two regions of the p53 gene and one region of k-ras. For k-ras and p53, bias was observed when the wild-type and mutant sequences were amplified separately or when mixed in equal proportions before PCR. Bias was present with proofreading and non-proofreading polymerase. Mutant and wild-type segments of the factor V, cystic fibrosis transmembrane conductance regulator and prothrombin genes were amplified and did not exhibit PCR bias. Therefore, the assumption of equal PCR efficiency for point mutant and wild-type sequences is invalid in several systems. Quantitative or diagnostic PCR will require validation for each locus, and enrichment strategies may be needed to optimize detection of mutants.

Are the ‘cave’ minerals archerite (K,NH4)H2PO4 and biphosphammite (K,NH4)H2PO4 identical? A molecular structural study

The molecular structure of the mineral archerite ((K,NH4)H2PO4) has been determined and compared with that of biphosphammite ((NH4,K)H2PO4). Raman spectroscopy and infrared spectroscopy has been used to characterise these ‘cave’ minerals. Both minerals originated from the Murra-el-elevyn Cave, Eucla, Western Australia. The mineral is formed by the reaction of the chemicals in bat guano with calcite substrates. Raman and infrared bands are assigned to H2PO4-, OH and NH stretching vibrations. The Raman band at 981 cm-1 is assigned to the HOP stretching vibration. Bands in the 1200 to 1800 cm-1 region are associated with NH4+ bending modes. The molecular structure of the two minerals appear to be very similar, and it is therefore concluded that the two minerals are identical.

The undirected feedback vertex set problem has a Poly(k) Kernel

Resolving a noted open problem, we show that the Undirected Feedback Vertex Set problem, parameterized by the size of the solution set of vertices, is in the parameterized complexity class Poly(k), that is, polynomial-time pre-processing is sufficient to reduce an initial problem instance (G, k) to a decision-equivalent simplified instance (G', k') where k' � k, and the number of vertices of G' is bounded by a polynomial function of k. Our main result shows an O(k11) kernelization bound.

Vibrational spectroscopic analysis of taranakite (K,NH4)Al3(PO4)3(OH) •9(H2O) from the Jenolan Caves, Australia

Many phosphate containing minerals are found in the Jenolan Caves. Such minerals are formed by the reaction of bat guano and clays from the caves. Among these cave minerals is the mineral taranakite (K,NH4)Al3(PO4)3(OH)•9(H2O) which has been identified by X-ray diffraction. Jenolan Caves taranakite has been characterised by Raman spectroscopy. Raman and infrared bands are assigned to H2PO4-, OH and NH stretching vibrations. By using a combination of XRD and Raman spectroscopy, the existence of taranakite in the caves has been proven.