Computer Sciences Commons^™

Bootstrapping Events And Relations From Text, Ting Liu

Legacy Theses & Dissertations (2009 - 2024)

Information Extraction (IE) is a technique for automatically extracting structured data from text documents. One of the key analytical tasks is extraction of important and relevant information from textual sources. While information is plentiful and readily available, from the Internet, news services, media, etc., extracting the critical nuggets that matter to business or to national security is a cognitively demanding and time consuming task. Intelligence and business analysts spend many hours poring over endless streams of text documents pulling out reference to entities of interest (people, locations, organizations) as well as their relationships as reported in text. Such extracted "information …

Detecting Malicious Software By Dynamicexecution, Jianyong Dai

Electronic Theses and Dissertations

Traditional way to detect malicious software is based on signature matching. However, signature matching only detects known malicious software. In order to detect unknown malicious software, it is necessary to analyze the software for its impact on the system when the software is executed. In one approach, the software code can be statically analyzed for any malicious patterns. Another approach is to execute the program and determine the nature of the program dynamically. Since the execution of malicious code may have negative impact on the system, the code must be executed in a controlled environment. For that purpose, we have …

An Enhanced Data Mining Life Cycle, Markus Hofmann, Brendan Tierney

Conference papers

Data mining projects are complex and can have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for data mining projects, its team members and their role. The paper provides a detailed view of the design and development of the data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic …

Sentiment Classification Of Reviews Using Sentiwordnet, Bruno Ohana, Brendan Tierney

Conference papers

Sentiment classification concerns the use of automatic methods for predicting the orientation of subjective content on text documents, with applications on a number of areas including recommender and advertising systems, customer intelligence and information retrieval. SentiWordNet is an opinion lexicon derived from the WordNet database where each term is associated with numerical scores indicating positive and negative sentiment information. This research presents the results of applying the SentiWordNet lexical resource to the problem of automatic sentiment classification of film reviews. Our approach comprises counting positive and negative term scores to determine sentiment orientation, and an improvement is presented by building …

Investigating Data Mining Techniques For Extracting Information From Alzheimer's Disease Data, Vinh Quoc Dang

Theses : Honours

Data mining techniques have been used widely in many areas such as business, science, engineering and more recently in clinical medicine. These techniques allow an enormous amount of high dimensional data to be analysed for extraction of interesting information as well as the construction of models for prediction. One of the foci in health related research is Alzheimer's disease which is currently a non-curable disease where diagnosis can only be confirmed after death via an autopsy. Using multi-dimensional data and the applications of data mining techniques, researchers hope to find biomarkers that will diagnose Alzheimer's disease as early as possible. …

Effects Of Similarity Metrics On Document Clustering, Rushikesh Veni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Document clustering or unsupervised document classification is an automated process of grouping documents with similar content. A typical technique uses a similarity function to compare documents. In the literature, many similarity functions such as dot product or cosine measures are proposed for the comparison operator.

For the thesis, we evaluate the effects a similarity function may have on clustering. We start by representing a document and a query, both as a vector of high-dimensional space corresponding to the keywords followed by using an appropriate distance measure in k-means to compute similarity between the document vector and the query vector to …

Word Sense Disambiguation In Biomedical Ontologies With Term Co-Occurrence Analysis And Document Clustering, Bill Andreopoulos, Dimitra Alexopoulou, Michael Schroeder

Faculty Publications, Computer Science

With more and more genomes being sequenced, a lot of effort is devoted to their annotation with terms from controlled vocabularies such as the GeneOntology. Manual annotation based on relevant literature is tedious, but automation of this process is difficult. One particularly challenging problem is word sense disambiguation. Terms such as |development| can refer to developmental biology or to the more general sense. Here, we present two approaches to address this problem by using term co-occurrences and document clustering. To evaluate our method we defined a corpus of 331 documents on development and developmental biology. Term co-occurrence analysis achieves an …

Symbolic Methodology For Numeric Data Mining, Boris Kovalerchuk, Engenii Vityaev

All Faculty Scholarship for the College of the Sciences

Currently statistical and artificial neural network methods dominate in data mining applications. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design, and other areas. Neural networks and decision tree methods have serious limitations in capturing relations that may have a variety of forms. Learning systems based on symbolic first-order logic (FOL) representations capture relations naturally. The learned regularities are understandable directly in domain terms that help to build a domain theory. This paper describes relational data mining methodology and develops it further for numeric data such as financial and spatial data. This includes (1) comparing …

Relational Methodology For Data Mining And Knowledge Discovery, Engenii Vityaev, Boris Kovalerchuk

All Faculty Scholarship for the College of the Sciences

Knowledge discovery and data mining methods have been successful in many domains. However, their abilities to build or discover a domain theory remain unclear. This is largely due to the fact that many fundamental KDD&DM methodological questions are still unexplored such as (1) the nature of the information contained in input data relative to the domain theory, and (2) the nature of the knowledge that these methods discover. The goal of this paper is to clarify methodological questions of KDD&DM methods. This is done by using the concept of Relational Data Mining (RDM), representative measurement theory, an ontology of a …

Using Plsi-U To Detect Insider Threats By Datamining Email, James S. Okolica, Gilbert L. Peterson, Robert F. Mills

Faculty Publications

Despite a technology bias that focuses on external electronic threats, insiders pose the greatest threat to an organisation. This paper discusses an approach to assist investigators in identifying potential insider threats. We discern employees' interests from e-mail using an extended version of PLSI. These interests are transformed into implicit and explicit social network graphs, which are used to locate potential insiders by identifying individuals who feel alienated from the organisation or have a hidden interest in a sensitive topic. By applying this technique to the Enron e-mail corpus, a small number of employees appear as potential insider threats.

The Impact Of Directionality In Predications On Text Mining, Gondy Leroy, Marcelo Fiszman, Thomas C. Rindflesch

CGU Faculty Publications and Research

The number of publications in biomedicine is increasing enormously each year. To help researchers digest the information in these documents, text mining tools are being developed that present co-occurrence relations between concepts. Statistical measures are used to mine interesting subsets of relations. We demonstrate how directionality of these relations affects interestingness. Support and confidence, simple data mining statistics, are used as proxies for interestingness metrics. We first built a test bed of 126,404 directional relations extracted from biomedical abstracts, which we represent as graphs containing a central starting concept and 2 rings of associated relations. We manipulated directionality in four …

Mobile Semantic Computing, Karthik Gomadam, Anupam Joshi, Amit P. Sheth

Kno.e.sis Publications

We propose to organize a special session on research in the intersection of mobile computing, the Semantic Web and Web services.

This session will examine how the research in these areas can serve as a foundation for new architectural and communication paradigms that can enhance service creation, distribution, discovery, integration and utilization in distributed and ubiquitous environments. Some of the initial areas that our early research have highlighted are :

1. Semantic annotation of data in bandwidth constrained environments such as mobile networks to promote efficient bandwidth utilization
2. Possibilities of using microformats such as RDFa and opportunities that can be explored …

Optrr: Optimizing Randomized Response Schemes For Privacy-Preserving Data Mining, Zhengli Huang, Wenliang Du

Electrical Engineering and Computer Science - All Scholarship

The randomized response (RR) technique is a promising technique to disguise private categorical data in Privacy-Preserving Data Mining (PPDM). Although a number of RR-based methods have been proposed for various data mining computations, no study has systematically compared them to find optimal RR schemes. The difficulty of comparison lies in the fact that to compare two PPDM schemes, one needs to consider two conflicting metrics: privacy and utility. An optimal scheme based on one metric is usually the worst based on the other metric. In this paper, we first describe a method to quantify privacy and utility. We formulate the …

Data Exploration By Using The Monotonicity Property, Hongyi Chen

LSU Master's Theses

Dealing with different misclassification costs has been a big problem for classification. Some algorithms can predict quite accurately when assuming the misclassification costs for each class are the same, like most rule induction methods. However, when the misclassification costs change, which is a common phenomenon in reality, these algorithms are not capable of adjusting their results. Some other algorithms, like the Bayesian methods, have the ability to yield probabilities of a certain unclassified example belonging to given classes, which is helpful to make modification on the results according to different misclassification costs. The shortcoming of such algorithms is, when the …

Automatically Extract Information From Web Documents, Dipesh Sharma

Masters Theses & Specialist Projects

The Internet could be considered to be a reservoir of useful information in textual form — product catalogs, airline schedules, stock market quotations, weather forecast etc. There has been much interest in building systems that gather such information on a user's behalf. But because these information resources are formatted differently, mechanically extracting their content is difficult. Systems using such resources typically use hand-coded wrappers, customized procedures for information extraction. Structured data objects are a very important type of information on the Web. Such data objects are often records from underlying databases and displayed in Web pages with some fixed templates. …

Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng

Research Collection School Of Computing and Information Systems

Coronary artery disease (CAD) is a main cause of death in the world. Finding cost-effective methods to predict CAD is a major challenge in public health. In this paper, we investigate the combined effects of genetic polymorphisms and non-genetic factors on predicting the risk of CAD by applying well known classification methods, such as Bayesian networks, naïve Bayes, support vector machine, k-nearest neighbor, neural networks and decision trees. Our experiments show that all these classifiers are comparable in terms of accuracy, while Bayesian networks have the additional advantage of being able to provide insights into the relationships among the variables. …

Multi-Class Classification Averaging Fusion For Detecting Steganography, Benjamin M. Rodriguez, Gilbert L. Peterson, Sos S. Agaian

Faculty Publications

Multiple classifier fusion has the capability of increasing classification accuracy over individual classifier systems. This paper focuses on the development of a multi-class classification fusion based on weighted averaging of posterior class probabilities. This fusion system is applied to the steganography fingerprint domain, in which the classifier identifies the statistical patterns in an image which distinguish one steganography algorithm from another. Specifically we focus on algorithms in which jpeg images provide the cover in order to communicate covertly. The embedding methods targeted are F5, JSteg, Model Based, OutGuess, and StegHide. The developed multi-class steganalvsis system consists of three levels: (1) …

An Investigation Into The Application Of Data Mining Techniques To Characterize Agricultural Soil Profiles, Rowan J. Maddern

Theses : Honours

The advances in computing and information storage have provided vast amounts of data. The challenge has been to extract knowledge from this raw data; this has led to new methods and techniques such as data mining that can bridge the knowledge gap. The research aims to use these new data mining techniques and apply them to a soil science database to establish if meaningful relationships can be found. A data set extracted from the WA Department of Agriculture and Food (DAFW A) soils database has been used to conduct this research. The database contains measurements of soil profile data from …

Bias And Controversy: Beyond The Statistical Deviation, Hady W. Lauw, Ee Peng Lim, Ke Wang

Research Collection School Of Computing and Information Systems

In this paper, we investigate how deviation in evaluation activities may reveal bias on the part of reviewers and controversy on the part of evaluated objects. We focus on a 'data-centric approach' where the evaluation data is assumed to represent the ground truth'. The standard statistical approaches take evaluation and deviation at face value. We argue that attention should be paid to the subjectivity of evaluation, judging the evaluation score not just on 'what is being said' (deviation), but also on 'who says it' (reviewer) as well as on 'whom it is said about' (object). Furthermore, we observe that bias …

Bi-Level Clustering Of Mixed Categorical And Numerical Biomedical Data, Bill Andreopoulos, Aijun An, Xiaogang Wang

Faculty Publications, Computer Science

Biomedical data sets often have mixed categorical and numerical types, where the former represent semantic information on the objects and the latter represent experimental results. We present the BILCOM algorithm for |Bi-Level Clustering of Mixed categorical and numerical data types|. BILCOM performs a pseudo-Bayesian process, where the prior is categorical clustering. BILCOM partitions biomedical data sets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations, more accurately than if using one type alone.

Enhancing Web Marketing By Using Ontology, Xuan Zhou

Dissertations

The existence of the Web has a major impact on people's life styles. Online shopping, online banking, email, instant messenger services, search engines and bulletin boards have gradually become parts of our daily life. All kinds of information can be found on the Web. Web marketing is one of the ways to make use of online information. By extracting demographic information and interest information from the Web, marketing knowledge can be augmented by applying data mining algorithms. Therefore, this knowledge which connects customers to products can be used for marketing purposes and for targeting existing and potential customers. The Web …

Temporal Data Mining In A Dynamic Feature Space, Brent K. Wenerstrom

Theses and Dissertations

Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a similar incremental learning algorithm.

Fisa: Feature-Based Instance Selection For Imbalanced Text Classification, Aixin Sun, Ee Peng Lim, Boualem Benatallah, Mahbub Hassan

Research Collection School Of Computing and Information Systems

Support Vector Machines (SVM) classifiers are widely used in text classification tasks and these tasks often involve imbalanced training. In this paper, we specifically address the cases where negative training documents significantly outnumber the positive ones. A generic algorithm known as FISA (Feature-based Instance Selection Algorithm), is proposed to select only a subset of negative training documents for training a SVM classifier. With a smaller carefully selected training set, a SVM classifier can be more efficiently trained while delivering comparable or better classification accuracy. In our experiments on the 20-Newsgroups dataset, using only 35% negative training examples and 60% learning …

Sgpm: Static Group Pattern Mining Using Apriori-Like Sliding Window, John Goh, David Taniar, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Mobile user data mining is a field that focuses on extracting interesting pattern and knowledge out from data generated by mobile users. Group pattern is a type of mobile user data mining method. In group pattern mining, group patterns from a given user movement database is found based on spatio-temporal distances. In this paper, we propose an improvement of efficiency using area method for locating mobile users and using sliding window for static group pattern mining. This reduces the complexity of valid group pattern mining problem. We support the use of static method, which uses areas and sliding windows instead …

Detecting Potential Insider Threats Through Email Datamining, James S. Okolica

Theses and Dissertations

No abstract provided.

Text Mining With Exploitation Of User's Background Knowledge : Discovering Novel Association Rules From Text, Xin Chen

Dissertations

The goal of text mining is to find interesting and non-trivial patterns or knowledge from unstructured documents. Both objective and subjective measures have been proposed in the literature to evaluate the interestingness of discovered patterns. However, objective measures alone are insufficient because such measures do not consider knowledge and interests of the users. Subjective measures require explicit input of user expectations which is difficult or even impossible to obtain in text mining environments.

This study proposes a user-oriented text-mining framework and applies it to the problem of discovering novel association rules from documents. The developed system, uMining, consists of two …

Data Mining Techniques To Study Therapy Success With Autistic Children, Gondy A. Leroy, Annika Irmscher, Marjorie H. Charlop

CGU Faculty Publications and Research

Autism spectrum disorder has become one of the most prevalent developmental disorders, characterized by a wide variety of symptoms. Many children need extensive therapy for years to improve their behavior and facilitate integration in society. However, few systematic evaluations are done on a large scale that can provide insights into how, where, and how therapy has an impact. We describe how data mining techniques can be used to provide insights into behavioral therapy as well as its effect on participants. To this end, we are developing a digital library of coded video segments that contains data on appropriate and inappropriate …

Application Of Information-Theoretic Data Mining Techniques In A National Ambulatory Practice Outcomes Research Network, Adam Wright, Thomas N. Ricciardi, Martin Zwick

Complex Systems Faculty Publications and Presentations

The Medical Quality Improvement Consortium data warehouse contains de-identified data on more than 3.6 million patients including their problem lists, test results, procedures and medication lists. This study uses reconstructability analysis, an information-theoretic data mining technique, on the MQIC data warehouse to empirically identify risk factors for various complications of diabetes including myocardial infarction and microalbuminuria. The risk factors identified match those risk factors identified in the literature, demonstrating the utility of the MQIC data warehouse for outcomes research, and RA as a technique for mining clinical data warehouses.

Keynote: The Use Of Meta-Heuristic Algorithms For Data Mining, Dr. Beatrize De La Iglesia, A. Reynolds

International Conference on Information and Communication Technologies

In this paper we explore the application of powerful optimisers known as metaheuristic algorithms to problems within the data mining domain. We introduce some well-known data mining problems, and show how they can be formulated as optimisation problems. We then review the use of metaheuristics in this context. In particular, we focus on the task of partial classification and show how multi-objective metaheuristics have produced results that are comparable to the best known techniques but more scalable to large databases. We conclude by reinforcing the importance of research on the areas of metaheuristics for optimisation and data mining. The combination …

A Dynamic Weight Assignment Approach For Ir Systems, M. Shoaib, Prof Dr. Abad Ali Shah, A. Vashishta

International Conference on Information and Communication Technologies

Weights are assigned to the extracted keywords for partial matching and computing ranking in an IR system. Weight assignment technique is suggested by the IR model that is used for an IR system. Currently suggested weight assignment techniques are static which means that once weight is assigned a keyword it remains unchanged during life-span of an IR system. In this paper, we suggest a dynamic weight assignment technique. This technique can be used by any IR model that supports partial matching.