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Data Mining - Concepts and Techniques.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Matching large-scale biomedical ontologies with central concept based partitioning algorithm and Adaptive Compact Evolutionary Algorithm

A large number of published datasets (or sources) that follow Linked Data principles is currently available and this number grows rapidly. However, the major target of Linked Data, i.e., linking and integration, is not easy to achieve. In general, information integration is difficult, because (a) datasets are produced, kept, or managed by different organizations using different models, schemas, or formats, (b) the same real-world entities or relationships are referred with different URIs or names and in different natural languages,(c) datasets usually contain complementary information, (d) datasets can contain data that are erroneous, out-of-date, or conflicting, (e) datasets even about the same domain may follow different conceptualizations of the domain, (f) everything can change (e.g., schemas, data) as time passes. This article surveys the work that has been done in the area of Linked Data integration, it identifies the main actors and use cases, it analyzes and factorizes the integration process according to various dimensions, and it discusses the methods that are used in each step. Emphasis is given on methods that can be used for integrating several datasets. Based on this analysis, the article concludes with directions that are worth further research.

Large-scale Semantic Integration of Linked Data: A Survey

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ontologies, automatic matchers become an inevitable solution. However, the process of matching large ontologies has high space and time complexities. Therefore, for a tool to efficiently and accurately match these large ontologies within the limited computing resources, it must have techniques that can significantly reduce the high space and time complexities associated with the ontology matching process. This article provides a review of the state-of-the-art techniques being applied by ontology matching tools to achieve scalability and produce high-quality mappings when matching large ontologies. In addition, we provide a direct comparison of the techniques to gauge their effectiveness in achieving scalability. A review of the state-of-the-art ontology matching tools that employ each strategy is also provided. We also evaluate the state-of-the-art tools to gauge the progress they have made over the years in improving alignment’s quality and reduction of execution time when matching large ontologies.

Large-Scale Ontology Matching: State-of-the-Art Analysis

The area of fraud detection1 has been traditionally correlated with data mining and text mining. Even before the "big data" phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper reviews the existing research done in fraud detection across different areas with the aim of investigating the machine learning techniques used and find out their strengths and weaknesses. It used the systematic quantitative literature review methodology to review the research studies in the field of fraud detection research within the last decade using machine learning techniques. Various combinations of keywords were used to identify the pertinent articles and were retrieved from ACM Digital Library, IEEE Xplorer Digital Library, Science Direct, Springer Link, etc. This search used a sample of 80 relevant articles (peer-reviewed journals articles and conference papers). The most used machine learning techniques were identified, and their strengths and weaknesses. Finally, the conclusion, limitations and future work have been shown.

A state-of-the-art review of machine learning techniques for fraud detection research

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated development of large independent ontologies within the same or different domains with some overlapping information among them. In order to match such large ontologies, automatic matchers become an inevitable solution. This work explores the use of a predictive statistical model to establish an alignment between two input ontologies. We demonstrate how to integrate ontology partitioning and parallelism in the ontology matching process in order to make the statistical predictive model scalable to large ontology matching tasks. Unlike most ontology matching tools which establish 1:1 cardinality mappings, our statistical model generates one-to-many cardinality mappings.

A statistically-based ontology matching tool

Feature selection is referred to the process of obtaining a subset from an original feature set according to certain feature selection criterion, which selects the relevant features of the dataset. It plays a role in compressing the data processing scale, where the redundant and irrelevant features are removed. Feature selection techniques show that more information is not always good in machine learning applications. Apply different algorithms for the data at hand and with baseline classification performance values we can select a final feature selection algorithm. In this paper, we propose a hybrid classification model, which has correlation based filter feature selection algorithm and Machine learning as classifiers. The objective of this study is to select relevant features and analyze the outperform machine learning algorithms in order to train our model, predict and compare their classification performance. In this method, features are ordered according to their Absolute correlation value with respect to the class attribute. Then top K Features are selected from ordered list of features to form a reduced dataset. This proposed classifier model is applied to our smart meter datasets. To measure the performance of these selected features; seven benchmark classifier are used; Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbor (kNN), Naive Bayes (NB), Decision Tree (DT), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). This paper then analyzes the performance of all classifiers with feature selection in term of accuracy, sensitivity, F-Measure, Specificity, Precision, and MCC. From our experiment, we found that Random Forest classifier performed higher than other used classifiers.

Hybrid model of correlation based filter feature selection and machine learning classifiers applied on smart meter data set

Ontology matching, the process of resolving heterogeneity between two ontologies consumes a lot of computing memory and time. This problem is exacerbated in large ontology matching tasks. To address the problem of time and space complexity in the matching process, ontology partitioning has been adopted as one of the methods, however, most ontology partitioning algorithms either produce incomplete partitions or are slow in the partitioning process hence eroding the benefits of the partitioning. In this paper, we demonstrate that spectral partitioning of an ontology can generate high quality partitions geared towards ontology matching.

Swaib Kyanda

Papers

Large-Scale Ontology Matching: State-of-the-Art Analysis

A state-of-the-art review of machine learning techniques for fraud detection research

A statistically-based ontology matching tool

Hybrid model of correlation based filter feature selection and machine learning classifiers applied on smart meter data set

A K-way spectral partitioning of an ontology for ontology matching