Jacob Kogan

University of Maryland, Baltimore County

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Charles Nicholas

University of Maryland, Baltimore County

E. Zeheb

Technion – Israel Institute of Technology

Yaakov Malinovsky

University of Maryland, Baltimore County

Marc Teboulle

Tel Aviv University

Leonid Naimark

BAE Systems (United States)

Michael W. Berry

University of Tennessee at Knoxville

J. Hocherman

Technion – Israel Institute of Technology

Maria Barouti

University of Maryland, Baltimore County

Daniel Keren

University of Haifa

Shmuel S. Oren

University of California, Berkeley

Cooperative Institutions

Technion – Israel Institute of Technology

University of Maryland, Baltimore County

Ben-Gurion University of the Negev

Barzilai Medical Center

University of Maryland, Baltimore

University of Maryland, College Park

University of Tennessee at Knoxville

University of California, Berkeley

Stanford University

Max Delbrück Center

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Text mining : applications and theory

John Wiley & Sons eBooks (2010)

Michael W. Berry Jacob Kogan

List of Contributors. Preface. PART I TEXT EXTRACTION, CLASSIFICATION, ANDCLUSTERING. 1 Automatic keyword extraction from individualdocuments. 1.1 Introduction. 1.2 Rapid automatic keyword extraction. 1.3 Benchmark evaluation. 1.4 Stoplist generation. 1.5 Evaluation on news articles. 1.6 Summary. 1.7 Acknowledgements. 2 Algebraic techniques for multilingual documentclustering. 2.1 Introduction. 2.2 Background. 2.3 Experimental setup. 2.4 Multilingual LSA. 2.5 Tucker1 method. 2.6 PARAFAC2 method. 2.7 LSA with term alignments. 2.8 Latent morpho-semantic analysis (LMSA). 2.9 LMSA with term alignments. 2.10 Discussion of results and techniques. 2.11 Acknowledgements. 3 Content-based spam email classification usingmachine-learning algorithms. 3.1 Introduction. 3.2 Machine-learning algorithms. 3.3 Data preprocessing. 3.4 Evaluation of email classification. 3.5 Experiments. 3.6 Characteristics of classifiers. 3.7 Concluding remarks. 3.8 Acknowledgements. 4 Utilizing nonnegative matrix factorization for emailclassification problems. 4.1 Introduction. 4.2 Background. 4.3 NMF initialization based on feature ranking. 4.4 NMF-based classification methods. 4.5 Conclusions. 4.6 Acknowledgements. 5 Constrained clustering with k-means typealgorithms. 5.1 Introduction. 5.2 Notations and classical k-means. 5.3 Constrained k-means with Bregman divergences. 5.4 Constrained smoka type clustering. 5.5 Constrained spherical k-means. 5.6 Numerical experiments. 5.7 Conclusion. PART II ANOMALY AND TREND DETECTION. 6 Survey of text visualization techniques. 6.1 Visualization in text analysis. 6.2 Tag clouds. 6.3 Authorship and change tracking. 6.4 Data exploration and the search for novel patterns. 6.5 Sentiment tracking. 6.6 Visual analytics and FutureLens. 6.7 Scenario discovery. 6.8 Earlier prototype. 6.9 Features of FutureLens. 6.10 Scenario discovery example: bioterrorism. 6.11 Scenario discovery example: drug trafficking. 6.12 Future work. 7 Adaptive threshold setting for novelty mining. 7.1 Introduction. 7.2 Adaptive threshold setting in novelty mining. 7.3 Experimental study. 7.4 Conclusion. 8 Text mining and cybercrime. 8.1 Introduction. 8.2 Current research in Internet predation andcyberbullying. 8.3 Commercial software for monitoring chat. 8.4 Conclusions and future directions. 8.5 Acknowledgements. PART III TEXT STREAMS. 9 Events and trends in text streams. 9.1 Introduction. 9.2 Text streams. 9.3 Feature extraction and data reduction. 9.4 Event detection. 9.5 Trend detection. 9.6 Event and trend descriptions. 9.7 Discussion. 9.8 Summary. 9.9 Acknowledgements. 10 Embedding semantics in LDA topic models. 10.1 Introduction. 10.2 Background. 10.3 Latent Dirichlet allocation. 10.4 Embedding external semantics from Wikipedia. 10.5 Data-driven semantic embedding. 10.6 Related work. 10.7 Conclusion and future work. References. Index.

Non-negative Matrix Factorization

K-Means Clustering

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Introduction to Clustering Large and High-Dimensional Data

Jacob Kogan

1. Introduction and motivation 2. Quadratic k-means algorithm 3. BIRCH 4. Spherical k-means algorithm 5. Linear algebra techniques 6. Information-theoretic clustering 7. Clustering with optimization techniques 8. k-means clustering with divergence 9. Assessment of clustering results 10. Appendix: Optimization and Linear Algebra Background 11. Solutions to selected problems.

Divergence (linguistics)

Clustering high-dimensional data

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Citations (172)

Grouping Multidimensional Data: Recent Advances in Clustering

Springer eBooks (2006)

Jacob Kogan Charles Nicholas Marc Teboulle

Data pre-processing

Clustering high-dimensional data

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Citations (134)

Robust Stability and Convexity: An Introduction

Jacob Kogan

and motivation.- Stability of box polynomial families.- Stability radii and convex analysis.- Multiaffine polynomial families.- Multidimensional systems and systems with commensurate delays.- Uncertain time-delay systems.- Convexity of frequency response arcs associated with Hurwitz qausipolynomials.- Epilogue.

Convexity

Hurwitz polynomial

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Feature Selection over Distributed Data Streams through Convex Optimization

Jacob Kogan

Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as frequency of occurrence of stream items, motivated by recent contributions based on geometric ideas we present an alternative approach. The proposed approach enables monitoring values of an arbitrary threshold function over distributed data streams through constraints applied separately on each stream. We report numerical experiments on a real-world data that detect instances where communication between nodes is required, and compare the approach and the results to those recently reported in the literature.

Feature (linguistics)

Distributed database

10.1137/1.9781611972825.41

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Citations (11)

Scalable Clustering with smoka

Jacob Kogan

The paper reports a multi-step clustering procedure equipped with a divergence (a distance like junction derived from a convex function). The first step of the procedure is a BIRCH like algorithm capable to convert very large datasets to "summaries" that require much less computer memory. The second step is the principal direction divisive partitioning algorithm (PDDP) that partitions the set of "summaries" into k clusters. This partition is the input for a smoothed k-means based clustering algorithm (smoka). The final partition of "summaries" generated by smoka induces a partition of the original dataset. Preliminary numerical experiments with text collections reported in the paper demonstrate smoka's remarkable accuracy and speed of convergence

Divergence (linguistics)

10.1109/iccta.2007.114

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Citations (1)

On the stability of quasipolynomials with weighted diamond coefficients

Multidimensional Systems and Signal Processing (1994)

J. Hocherman Vladimir L. Kharitonov Jacob Kogan E. Zeheb

Constant (computer programming)

Extreme point

10.1007/bf00989280

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Citations (5)