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Tactical Combat Causality Care (TCCC) card is used by the combat medic who arrives at the battlefield location and analyzes the injuries, by documenting the treatment information and completing the field medical card. This field medical card is a standard method of communication between the combat medic and the combat physician who performs further treatments on the wounded soldier after retrieving injury records and treatment information from TCCC card. This process is time consuming and occupies the medics' hands preventing them from focusing on the treatment of the wounded soldier. This paper proposes a more efficient way to assist the combat medic by recording the audio and then converting it into text and then using information extraction techniques and pattern matching algorithms to automatically complete the TCCC card. This paper describes the proposed system and an algorithm to extract values for the TCCC card. The extracted values are then stored in a XML file and later parsed and stored in as a JAVA object. [ABSTRACT FROM AUTHOR]

Attribute reduction is one of the key issues in rough set theory, and many algorithms have been proposed for static data set. Very little work has been done for incremental attribute reduction algorithm in era of big data. In this paper, the weakness of the existing incremental attribute reduction are analyzed. Then, two strategies of incremental attribute reduction algorithm for big data are designed, and an incremental attribute reduction algorithm using MapReduce is proposed. In order to reduce the computational complexity, our algorithm reuse the former Map results to speedup the computations of the equivalence classes. A new reduct can be updated by the old reduct effectively. This study gives some insights into how to conduct incremental attribute reduction for big data. [ABSTRACT FROM AUTHOR]

With technological advancements, massive amount of data is being collected in various domains. For instance, since the advent of digital image technology and remote sensing imagery (RSI), NASA and U.S. Geological Survey through the Landsat Data Continuity Mission, has been capturing images of Earth down to 15 meters resolution. Likewise, consider the Internet, where, growth of social media, blog Web sites , etc. generates exponential amount of textual data on a daily basis. Since clustering of data is time-consuming, much of these data is archived even before proper analysis. In this paper, we propose two novel and extremely fast algorithms called imgFAUST or Fast Attribute-based Unsupervised and Supervised Table Clustering for images and a variation called docFAUST for textual data. Both these algorithms are based on Predicate-Trees which are compressed, lossless and data-mining-ready data structures. Without compromising much on the accuracy, our algorithms are fast and can be effectively used in high-speed image data and document analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient clustering algorithm for analyzing categorical data streams. It has been proved that the proposed algorithm uses small memory footprints. We provide empirical analysis on the performance of the algorithm in clustering both synthetic and real data streams. [ABSTRACT FROM AUTHOR]

In this paper we present a majority-based method to learn Bayesian network structure from databases distributed over a peer-to-peer network. The method consists of a majority learning algorithm and a majority consensus protocol. The majority learning algorithm discovers the local Bayesian network structure based on the local database and updates the structure once new edges are learnt from neighboring nodes. The majority consensus protocol is responsible for the exchange of the local Bayesian networks between neighboring nodes. The protocol and algorithm are executed in tandem on each node. They perform their operations asynchronously and exhibit local communications. Simulation results verify that all new edges, except for edges with confidence levels close to the confidence threshold, can be discovered by exchange of messages with a small number of neighboring nodes. [ABSTRACT FROM AUTHOR]

Objectives: Modern clinicians use scalable data mining models to evaluate their hypotheses. The purpose of this paper is to present the lessons learned in solving prognostic problems in Intensive Care Unit (ICU) by using data mining models developed with standardized algorithms as an alternative solution to clinical assessment tools. Methods: The study included data from 201 ICU patients (156 male and 45 female) that were assessed by means of the APACHE II, the SOFA and the ISS as well as free thyroxin fT4, total triiodothyronine (TT3) T3, thyrotropin (TSH), corticotropin (ACTH), prolactin, cortisol and dehydroepiandrosterone sulphate (DHEAS) and the Synacthen test. We formulated three data mining models – a decision tree (DTM), a neural network (NNM), and a linear regression (LRM)- using the standardized algorithms of Microsoft™ SQL Server 2005 Data Mining Platform. The outcomes were compared against those of ICU clinical assessment tools and hormone measurements. Results: From the ROC plot analysis the APACHE II score was only marginally better than the SOFA or ISS score in predicting ICU survival. Moreover, the standardized data mining models applied on endocrine parameters were not outperformed by the APACHE II, SOFA or ISS scores alone in predicting ICU survival. Conclusions: From negative results, useful information can always be deduced. Our results point to the need to use custom algorithms to support particular ICU mining needs in lieu of standardized algorithms. [ABSTRACT FROM AUTHOR]

Web log analysis can provide valuable information about user behaviour when visiting a web site. Analysing users' traversal patterns and frequency or repetition of visits can provide very valuable information. However, the amount of information contained in web logs is huge. Primarily based on the Web servers' log files, the main objective of traversal pattern mining is to discover the frequent patterns in users' browsing paths and behaviours. Constraints have not been considered extensively as part of pre-mining tasks in web mining. This paper presents a complete framework for web usage mining, allowing users to pre-define constraints when analysing complex traversal patterns in order to improve the efficiency of algorithms and offer flexibility in producing results. [ABSTRACT FROM AUTHOR]