A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity

Global distributions of Phaeocystis sp. abundance and biomass - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The planktonic haptophyte Phaeocystis has been suggested to play a fundamental role in the global biogeochemical cycling of carbon and sulphur, but little is known about its global biomass distribution. We have collected global microscopy data of the genus Phaeocystis and converted abundance data to carbon biomass using species-specific carbon conversion factors. Microscopic counts of single-celled and colonial Phaeocystis were obtained both through the mining of online databases and by accepting direct submissions (both published and unpublished) from Phaeocystis specialists. We recorded abundance data from a total of 1595 depth-resolved stations sampled between 1955-2009. The quality-controlled dataset includes 5057 counts of individual Phaeocystis cells resolved to species level and information regarding life-stages from 3526 samples. 83% of stations were located in the Northern Hemisphere while 17% were located in the Southern Hemisphere. Most data were located in the latitude range of 50-70° N. While the seasonal distribution of Northern Hemisphere data was well-balanced, Southern Hemisphere data was biased towards summer months. Mean species- and form-specific cell diameters were determined from previously published studies. Cell diameters were used to calculate the cellular biovolume of Phaeocystis cells, assuming spherical geometry. Cell biomass was calculated using a carbon conversion factor for Prymnesiophytes (Menden-Deuer and Lessard, 2000). For colonies, the number of cells per colony was derived from the colony volume. Cell numbers were then converted to carbon concentrations. An estimation of colonial mucus carbon was included a posteriori, assuming a mean colony size for each species. Carbon content per cell ranged from 9 pg (single-celled Phaeocystis antarctica) to 29 pg (colonial Phaeocystis globosa). Non-zero Phaeocystis cell biomasses (without mucus carbon) range from 2.9 - 10?5 µg l-1 to 5.4 - 103 µg l-1, with a mean of 45.7 µg l-1 and a median of 3.0 µg l-1. Highest biomasses occur in the Southern Ocean below 70° S (up to 783.9 µg l-1), and in the North Atlantic around 50° N (up to 5.4 - 103 µg l-1).

Algorithms for efficient transaction management and consistent queries in client -server semantic object-oriented parallel databases

Large read-only or read-write transactions with a large read set and a small write set constitute an important class of transactions used in such applications as data mining, data warehousing, statistical applications, and report generators. Such transactions are best supported with optimistic concurrency, because locking of large amounts of data for extended periods of time is not an acceptable solution. The abort rate in regular optimistic concurrency algorithms increases exponentially with the size of the transaction. The algorithm proposed in this dissertation solves this problem by using a new transaction scheduling technique that allows a large transaction to commit safely with significantly greater probability that can exceed several orders of magnitude versus regular optimistic concurrency algorithms. A performance simulation study and a formal proof of serializability and external consistency of the proposed algorithm are also presented.^ This dissertation also proposes a new query optimization technique (lazy queries). Lazy Queries is an adaptive query execution scheme which optimizes itself as the query runs. Lazy queries can be used to find an intersection of sub-queries in a very efficient way, which does not require full execution of large sub-queries nor does it require any statistical knowledge about the data.^ An efficient optimistic concurrency control algorithm used in a massively parallel B-tree with variable-length keys is introduced. B-trees with variable-length keys can be effectively used in a variety of database types. In particular, we show how such a B-tree was used in our implementation of a semantic object-oriented DBMS. The concurrency control algorithm uses semantically safe optimistic virtual "locks" that achieve very fine granularity in conflict detection. This algorithm ensures serializability and external consistency by using logical clocks and backward validation of transactional queries. A formal proof of correctness of the proposed algorithm is also presented. ^

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Derivation of base-line geochemistry, petrography, and isotopic data for the host rocks to the Lucky Strike deposit and comparison with data from other alteration zones, Buchans Mining Camp, Newfoundland

The Buchans ore bodies of central Newfoundland represent some of the highest grade VMS deposits ever mined. These Kuroko-type deposits are also known for the well developed and preserved nature of the mechanically transported deposits. The deposits are hosted in Cambro-Ordovician, dominantly calc-alkaline, bimodal volcanic and epiclastic sequences of the Notre Dame Subzone, Newfoundland Appalachians. Stratigraphic relationships in this zone are complicated by extensively developed, brittledominated Silurian thrust faulting. Hydrothermal alteration of host rocks is a common feature of nearly all VMS deposits, and the recognition of these zones has been a key exploration tool. Alteration of host rocks has long been described to be spatially associated with the Buchans ore bodies, most notably with the larger in-situ deposits. This report represents a base-line study in which a complete documentation of the geochemical variance, in terms of both primary (igneous) and alteration effects, is presented from altered volcanic rocks in the vicinity of the Lucky Strike deposit (LSZ), the largest in-situ deposit in the Buchans camp. Packages of altered rocks also occur away from the immediate mining areas and constitute new targets for exploration. These zones, identified mostly by recent and previous drilling, represent untested targets and include the Powerhouse (PHZ), Woodmans Brook (WBZ) and Airport (APZ) alteration zones, as well as the Middle Branch alteration zone (MBZ), which represents a more distal alteration facies related to Buchans ore-formation. Data from each of these zones were compared to those from the LSZ in order to evaluate their relative propectivity. Derived litho geochemical data served two functions: (i) to define primary (igneous) trends and (ii) secondary alteration trends. Primary trends were established using immobile, or conservative, elements (i. e., HFSE, REE, Th, Ti0₂, Al₂0₃, P₂0₅). From these, altered volcanic rocks were interpreted in terms of composition (e.g., basalt - rhyodacite) and magmatic affinity (e.g., calc-alkaline vs. tholeiitic). The information suggests that bimodality is a common feature of all zones, with most rocks plotting as either basalt/andesite or dacite (or rhyodacite); andesitic senso stricto compositions are rare. Magmatic affinities are more varied and complex, but indicate that all units are arc volcanic sequences. Rocks from the LSZ/MBZ represent a transitional to calc-alkalic sequence, however, a slight shift in key geochemical discriminants occurs between the foot-wall to the hanging-wall. Specifically, mafic and felsic lavas of the foot-wall are of transitional (or mildly calc-alkaline) affinity whereas the hanging-wall rocks are relatively more strongly calc-alkaline as indicated by enriched LREE/HREE and higher ZrN, NbN and other ratios in the latter. The geochemical variations also serve as a means to separate the units (at least the felsic rocks) into hanging-wall and foot-wall sequences, therefore providing a valuable exploration tool. Volcanic rocks from the WBZ/PHZ (and probably the APZ) are more typical of tholeiitic to transitional suites, yielding flatter mantlenormalized REE patterns and lower ZrN ratios. Thus, the relationships between the immediate mining area (represented by LSZ/MBZ) and the Buchans East (PHZ/WBZ) and the APZ are uncertain. Host rocks for all zones consist of mafic to felsic volcanic rocks, though the proportion of pyroclastic and epiclastic rocks, is greatest at the LSZ. Phenocryst assemblages and textures are common in all zones, with minor exceptions, and are not useful for discrimination purposes. Felsic rocks from all zones are dominated by sericiteclay+/- silica alteration, whereas mafic rocks are dominated by chlorite- quartz- sericite alteration. Pyrite is ubiquitous in all moderately altered rocks and minor associated base metal sulphides occur locally. The exception is at Lucky Strike, where stockwork quartzveining contains abundant base-metal mineralization and barite. Rocks completely comprised of chlorite (chloritite) also occur in the LSZ foot-wall. In addition, K-feldspar alteration occurs in felsic volcanic rocks at the MBZ associated with Zn-Pb-Ba and, notably, without chlorite. This zone represents a peripheral, but proximal, zone of alteration induced by lower temperature hydrothermal fluids, presumably with little influence from seawater. Alteration geochemistry was interpreted from raw data as well as from mass balanced (recalculated) data derived from immobile element pairs. The data from the LSZ/MBZ indicate a range in the degree of alteration from only minor to severe modification of precursor compositions. Ba tends to show a strong positive correlation with K₂0, although most Ba occurs as barite. With respect to mass changes, Al₂0₃, Ti0₂ and P₂0₅ were shown to be immobile. Nearly all rocks display mass loss of Na₂O, CaO, and Sr reflecting feldspar destruction. These trends are usually mirrored by K₂0-Rb and MgO addition, indicating sericitic and chloritic alteration, respectively. More substantial gains ofK₂0 often occur in rocks with K-feldspar alteration, whereas a few samples also displayed excessive MgO enrichment and represent chloritites. Fe₂0₃ indicates both chlorite and sulphide formation. Si0₂ addition is almost always the case for the altered mafic rocks as silica often infills amygdules and replaces the finer tuffaceous material. The felsic rocks display more variability in Si0₂. Silicic, sericitic and chloritic alteration trends were observed from the other zones, but not K-feldspar, chloritite, or barite. Microprobe analysis of chlorites, sericites and carbonates indicate: (i) sericites from all zones are defined as muscovite and are not phengitic; (ii) at the LSZ, chlorites ranged from Fe-Mg chlorites (pycnochlorite) to Mg-rich chlorite (penninite), with the latter occurring in the stockwork zone and more proximal alteration facies; (iii) chlorites from the WBZ were typical of those from the more distal alteration facies of the LSZ, plotting as ripidolite to pycnochlorite; (iv) conversely, chlorite from the PHZ plot with Mg-Al-rich compositions (chlinochlore to penninite); and (v) carbonate species from each zone are also varied, with calcite occurring in each zone, in addition to dolomite and ankerite in the PHZ and WBZ, respectively. Lead isotope ratios for galena separates from the different various zones, when combined with data from older studies, tend to cluster into four distinctive fields. Overall, the data plot on a broad mixing line and indicate evolution in a relatively low-μ environment. Data from sulphide stringers in altered MBZ rocks, as well as from clastic sulphides (Sandfill prospect), plot in the Buchans ore field, as do the data for galena from altered rocks in the APZ. Samples from the Buchans East area are even more primitive than the Buchans ores, with lead from the PHZ plotting with the Connel Option prospect and data from the WBZ matching that of the Skidder prospect. A sample from a newly discovered debris flow-type sulphide occurrence (Middle Branch East) yields lead isotope ratios that are slightly more radiogenic than Buchans and plot with the Mary March alteration zone. Data within each cluster are interpreted to represent derivation from individual hydrothermal systems in which metals were derived from a common source.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Using Agents for Mining Maintenance Data while interacting in 3D Objectoriented Virtual Environments

This report demonstrates the development of: (a) object-oriented representation to provide 3D interactive environment using data provided by Woods Bagot; (b) establishing basis of agent technology for mining building maintenance data, and (C) 3D interaction in virtual environments using object-oriented representation. Applying data mining over industry maintenance database has been demonstrated in the previous report.

Mining patterns and factors contributing to crash severity on road curves

Road curves are an important feature of road infrastructure and many serious crashes occur on road curves. In Queensland, the number of fatalities is twice as many on curves as that on straight roads. Therefore, there is a need to reduce drivers’ exposure to crash risk on road curves. Road crashes in Australia and in the Organisation for Economic Co-operation and Development(OECD) have plateaued in the last five years (2004 to 2008) and the road safety community is desperately seeking innovative interventions to reduce the number of crashes. However, designing an innovative and effective intervention may prove to be difficult as it relies on providing theoretical foundation, coherence, understanding, and structure to both the design and validation of the efficiency of the new intervention. Researchers from multiple disciplines have developed various models to determine the contributing factors for crashes on road curves with a view towards reducing the crash rate. However, most of the existing methods are based on statistical analysis of contributing factors described in government crash reports. In order to further explore the contributing factors related to crashes on road curves, this thesis designs a novel method to analyse and validate these contributing factors. The use of crash claim reports from an insurance company is proposed for analysis using data mining techniques. To the best of our knowledge, this is the first attempt to use data mining techniques to analyse crashes on road curves. Text mining technique is employed as the reports consist of thousands of textual descriptions and hence, text mining is able to identify the contributing factors. Besides identifying the contributing factors, limited studies to date have investigated the relationships between these factors, especially for crashes on road curves. Thus, this study proposed the use of the rough set analysis technique to determine these relationships. The results from this analysis are used to assess the effect of these contributing factors on crash severity. The findings obtained through the use of data mining techniques presented in this thesis, have been found to be consistent with existing identified contributing factors. Furthermore, this thesis has identified new contributing factors towards crashes and the relationships between them. A significant pattern related with crash severity is the time of the day where severe road crashes occur more frequently in the evening or night time. Tree collision is another common pattern where crashes that occur in the morning and involves hitting a tree are likely to have a higher crash severity. Another factor that influences crash severity is the age of the driver. Most age groups face a high crash severity except for drivers between 60 and 100 years old, who have the lowest crash severity. The significant relationship identified between contributing factors consists of the time of the crash, the manufactured year of the vehicle, the age of the driver and hitting a tree. Having identified new contributing factors and relationships, a validation process is carried out using a traffic simulator in order to determine their accuracy. The validation process indicates that the results are accurate. This demonstrates that data mining techniques are a powerful tool in road safety research, and can be usefully applied within the Intelligent Transport System (ITS) domain. The research presented in this thesis provides an insight into the complexity of crashes on road curves. The findings of this research have important implications for both practitioners and academics. For road safety practitioners, the results from this research illustrate practical benefits for the design of interventions for road curves that will potentially help in decreasing related injuries and fatalities. For academics, this research opens up a new research methodology to assess crash severity, related to road crashes on curves.

Methods and preliminary results for a data linkage project to determine long-term survival after intensive care unit admission

Aims: To describe a local data linkage project to match hospital data with the Australian Institute of Health and Welfare (AIHW) National Death Index (NDI) to assess longterm outcomes of intensive care unit patients. Methods: Data were obtained from hospital intensive care and cardiac surgery databases on all patients aged 18 years and over admitted to either of two intensive care units at a tertiary-referral hospital between 1 January 1994 and 31 December 2005. Date of death was obtained from the AIHW NDI by probabilistic software matching, in addition to manual checking through hospital databases and other sources. Survival was calculated from time of ICU admission, with a censoring date of 14 February 2007. Data for patients with multiple hospital admissions requiring intensive care were analysed only from the first admission. Summary and descriptive statistics were used for preliminary data analysis. Kaplan-Meier survival analysis was used to analyse factors determining long-term survival. Results: During the study period, 21 415 unique patients had 22 552 hospital admissions that included an ICU admission; 19 058 surgical procedures were performed with a total of 20 092 ICU admissions. There were 4936 deaths. Median follow-up was 6.2 years, totalling 134 203 patient years. The casemix was predominantly cardiac surgery (80%), followed by cardiac medical (6%), and other medical (4%). The unadjusted survival at 1, 5 and 10 years was 97%, 84% and 70%, respectively. The 1-year survival ranged from 97% for cardiac surgery to 36% for cardiac arrest. An APACHE II score was available for 16 877 patients. In those discharged alive from hospital, the 1, 5 and 10-year survival varied with discharge location. Conclusions: ICU-based linkage projects are feasible to determine long-term outcomes of ICU patients

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.