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50 selected papers in Data Mining and Machine Learning

Here is the list of 50 selected papers in Data Mining and Machine Learning . You can download them for your detailed reading and research. Enjoy!

Data Mining and Statistics: What’s the Connection?

Data Mining: Statistics and More? , D. Hand, American Statistician, 52(2):112-118.

Data Mining , G. Weiss and B. Davison, in Handbook of Technology Management, John Wiley and Sons, expected 2010.

From Data Mining to Knowledge Discovery in Databases , U. Fayyad, G. Piatesky-Shapiro & P. Smyth, AI Magazine, 17(3):37-54, Fall 1996.

Mining Business Databases , Communications of the ACM, 39(11): 42-48.

10 Challenging Problems in Data Mining Research , Q. Yiang and X. Wu, International Journal of Information Technology & Decision Making, Vol. 5, No. 4, 2006, 597-604.

The Long Tail , by Anderson, C., Wired magazine.

AOL’s Disturbing Glimpse Into Users’ Lives , by McCullagh, D., News.com, August 9, 2006

General Data Mining Methods and Algorithms

Top 10 Algorithms in Data Mining , X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. motoda, G.J. MClachlan, A. Ng, B. Liu, P.S. Yu, Z. Zhou, M. Steinbach, D. J. Hand, D. Steinberg, Knowl Inf Syst (2008) 141-37.

Induction of Decision Trees , R. Quinlan, Machine Learning, 1(1):81-106, 1986.

Web and Link Mining

The Pagerank Citation Ranking: Bringing Order to the Web , L. Page, S. Brin, R. Motwani, T. Winograd, Technical Report, Stanford University, 1999.

The Structure and Function of Complex Networks , M. E. J. Newman, SIAM Review, 2003, 45, 167-256.

Link Mining: A New Data Mining Challenge , L. Getoor, SIGKDD Explorations, 2003, 5(1), 84-89.

Link Mining: A Survey , L. Getoor, SIGKDD Explorations, 2005, 7(2), 3-12.

Semi-supervised Learning

Semi-Supervised Learning Literature Survey , X. Zhu, Computer Sciences TR 1530, University of Wisconsin — Madison.

Introduction to Semi-Supervised Learning, in Semi-Supervised Learning (Chapter 1) O. Chapelle, B. Scholkopf, A. Zien (eds.), MIT Press, 2006. (Fordham’s library has online access to the entire text)

Learning with Labeled and Unlabeled Data , M. Seeger, University of Edinburgh (unpublished), 2002.

Person Identification in Webcam Images: An Application of Semi-Supervised Learning , M. Balcan, A. Blum, P. Choi, J. lafferty, B. Pantano, M. Rwebangira, X. Zhu, Proceedings of the 22nd ICML Workshop on Learning with Partially Classified Training Data , 2005.

Learning from Labeled and Unlabeled Data: An Empirical Study across Techniques and Domains , N. Chawla, G. Karakoulas, Journal of Artificial Intelligence Research , 23:331-366, 2005.

Text Classification from Labeled and Unlabeled Documents using EM , K. Nigam, A. McCallum, S. Thrun, T. Mitchell, Machine Learning , 39, 103-134, 2000.

Self-taught Learning: Transfer Learning from Unlabeled Data , R. Raina, A. Battle, H. Lee, B. Packer, A. Ng, in Proceedings of the 24th International Conference on Machine Learning , 2007.

An iterative algorithm for extending learners to a semisupervised setting , M. Culp, G. Michailidis, 2007 Joint Statistical Meetings (JSM), 2007

Partially-Supervised Learning / Learning with Uncertain Class Labels

Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers , V. Sheng, F. Provost, P. Ipeirotis, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2008.

Logistic Regression for Partial Labels , in 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems , Volume III, pp. 1935-1941, 2002.

Classification with Partial labels , N. Nguyen, R. Caruana, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2008.

Imprecise and Uncertain Labelling: A Solution based on Mixture Model and Belief Functions, E. Come, 2008 (powerpoint slides).

Induction of Decision Trees from Partially Classified Data Using Belief Functions , M. Bjanger, Norweigen University of Science and Technology, 2000.

Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth , P. Smyth, M. Burl, U. Fayyad, P. Perona, KDD Workshop 1994, AAAI Technical Report WS-94-03, pp. 109-120, 1994.

Recommender Systems

Trust No One: Evaluating Trust-based Filtering for Recommenders , J. O’Donovan and B. Smyth, In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI-05), 2005, 1663-1665.

Trust in Recommender Systems, J. O’Donovan and B. Symyth, In Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI-05), 2005, 167-174.

General resources available on this topic :

ICML 2003 Workshop: Learning from Imbalanced Data Sets II

AAAI ‘2000 Workshop on Learning from Imbalanced Data Sets

A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data , G. Batista, R. Prati, and M. Monard, SIGKDD Explorations , 6(1):20-29, 2004.

Class Imbalance versus Small Disjuncts , T. Jo and N. Japkowicz, SIGKDD Explorations , 6(1): 40-49, 2004.

Extreme Re-balancing for SVMs: a Case Study , B. Raskutti and A. Kowalczyk, SIGKDD Explorations , 6(1):60-69, 2004.

A Multiple Resampling Method for Learning from Imbalanced Data Sets , A. Estabrooks, T. Jo, and N. Japkowicz, in Computational Intelligence , 20(1), 2004.

SMOTE: Synthetic Minority Over-sampling Technique , N. Chawla, K. Boyer, L. Hall, and W. Kegelmeyer, Journal of Articifial Intelligence Research , 16:321-357.

Generative Oversampling for Mining Imbalanced Datasets, A. Liu, J. Ghosh, and C. Martin, Third International Conference on Data Mining (DMIN-07), 66-72.

Learning from Little: Comparison of Classifiers Given Little of Classifiers given Little Training , G. Forman and I. Cohen, in 8th European Conference on Principles and Practice of Knowledge Discovery in Databases , 161-172, 2004.

Issues in Mining Imbalanced Data Sets – A Review Paper , S. Visa and A. Ralescu, in Proceedings of the Sixteen Midwest Artificial Intelligence and Cognitive Science Conference , pp. 67-73, 2005.

Wrapper-based Computation and Evaluation of Sampling Methods for Imbalanced Datasets , N. Chawla, L. Hall, and A. Joshi, in Proceedings of the 1st International Workshop on Utility-based Data Mining , 24-33, 2005.

C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , C. Drummond and R. Holte, in ICML Workshop onLearning from Imbalanced Datasets II , 2003.

C4.5 and Imbalanced Data sets: Investigating the effect of sampling method, probabilistic estimate, and decision tree structure , N. Chawla, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Class Imbalances: Are we Focusing on the Right Issue?, N. Japkowicz, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Learning when Data Sets are Imbalanced and When Costs are Unequal and Unknown , M. Maloof, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Uncertainty Sampling Methods for One-class Classifiers , P. Juszcak and R. Duin, in ICML Workshop on Learning from Imbalanced Datasets II , 2003.

Active Learning

Improving Generalization with Active Learning , D Cohn, L. Atlas, and R. Ladner, Machine Learning 15(2), 201-221, May 1994.

On Active Learning for Data Acquisition , Z. Zheng and B. Padmanabhan, In Proc. of IEEE Intl. Conf. on Data Mining, 2002.

Active Sampling for Class Probability Estimation and Ranking , M. Saar-Tsechansky and F. Provost, Machine Learning 54:2 2004, 153-178.

The Learning-Curve Sampling Method Applied to Model-Based Clustering , C. Meek, B. Thiesson, and D. Heckerman, Journal of Machine Learning Research 2:397-418, 2002.

Active Sampling for Feature Selection , S. Veeramachaneni and P. Avesani, Third IEEE Conference on Data Mining, 2003.

Heterogeneous Uncertainty Sampling for Supervised Learning , D. Lewis and J. Catlett, In Proceedings of the 11th International Conference on Machine Learning, 148-156, 1994.

Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , G. Weiss and F. Provost, Journal of Artificial Intelligence Research, 19:315-354, 2003.

Active Learning using Adaptive Resampling , KDD 2000, 91-98.

Cost-Sensitive Learning

Types of Cost in Inductive Concept Learning , P. Turney, In Proceedings Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning.

Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection , P. Chan and S. Stolfo, KDD 1998.

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Prediction of Skin Diseases Using Machine Learning

Skin disease rates have been increasing over the past few decades. It has led to both fatal and non-fatal disabilities all around the world, especially in those areas where medical resources are not good enough. Early diagnosis of skin diseases increases the chances of cure significantly. Therefore, this work is comparing six machine learning algorithms, namely KNN, random forest, neural network, naïve bayes, logistic regression, and SVM, for the prediction of the skin diseases. The information gain, gain ratio, gini decrease, chi-square, and relieff are used to rank the features. This work comprises the introduction, literature review, and proposed methodology parts. In this research paper, a new method of analyzing skin disease has been proposed in which six different data mining techniques are used to develop an ensemble method that integrates all the six data mining techniques as a single one. The ensemble method used on the dermatology dataset gives improved result with 94% accuracy in comparison to other classifier algorithms and hence is more effective in this area.

A Survey on Building Recommendation Systems Using Data Mining Techniques

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

A Classification and Clustering Approach Using Data Mining Techniques in Analysing Gastrointestinal Tract

Diagnosis and detection of plant diseases using data mining techniques, location-based crime prediction using multiclass classification data mining techniques, an effective approach to test suite reduction and fault detection using data mining techniques.

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.

Applying data mining techniques to classify patients with suspected hepatitis C virus infection

Dengue fever prediction modelling using data mining techniques, fake news detection using data mining techniques.

Nowadays, internet has been well known as an information source where the information might be real or fake. Fake news over the web exist since several years. The main challenge is to detect the truthfulness of the news. The motive behind writing and publishing the fake news is to mislead the people. It causes damage to an agency, entity or person. This paper aims to detect fake news using semantic search.

A Leading Indicator Approach with Data Mining Techniques in Analysing Bitcoin Market Value

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The plan to mine the world’s research papers

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Priyanka Pulla is a freelance journalist based in Bengaluru, India.

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Carl Malamud in front of the data store of 73 million articles that he plans to let scientists text mine. Credit: Smita Sharma for Nature

Carl Malamud is on a crusade to liberate information locked up behind paywalls — and his campaigns have scored many victories. He has spent decades publishing copyrighted legal documents, from building codes to court records, and then arguing that such texts represent public-domain law that ought to be available to any citizen online. Sometimes, he has won those arguments in court. Now, the 60-year-old American technologist is turning his sights on a new objective: freeing paywalled scientific literature. And he thinks he has a legal way to do it.

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Applying Data Mining Research Methodologies on Information Systems

In this paper we considered several frameworks for data mining. These frameworks are based on different approaches, including inductive databases approach, the reductionist statistical approaches, data compression approach, constructive induction approach and some others. We considered advantages and limitations of these frameworks. We presented the view on data mining research as continuous and never- ending development process of an adaptive DM system towards the efficient utilization of available DM techniques for solving a current problem impacted by the dynamically changing environment. We discussed one of the traditional information systems frameworks and, drawing the analogy to this framework, we considered a data mining system as the special kind of adaptive information system. We adapted the information systems development framework for the context of data-mining systems development. Key words: Data Mining, Information Systems, Knowledge Discovery Databases

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Abstract Data mining applications are typically used in the decision making process. The knowledge discovery process (KDD process for short) is a typical iterative process, in which not only the raw data can be mined several times, but also the mined patterns might constitute the starting point for further mining on them.

Data Mining and Knowledge Discovery

Jean-François Boulicaut

Discovery Science

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Lecture Notes in Computer Science

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Data Mining Workshops, …

Motivated by the need for unification of the field of data mining and the growing demand for formalized representation of outcomes of research, we address the task of constructing an ontology of data mining. The proposed ontology, named OntoDM, is based on a recent proposal of a general framework for data mining, and includes definitions of basic data mining entities, such as datatype and dataset, data mining task, data mining algorithm and components thereof (e.g., distance function), etc. It also allows for the definition of more complex entities, e.g., constraints in constraint-based data mining, sets of such constraints (inductive queries) and data mining scenarios (sequences of inductive queries). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is a deep/heavy-weight ontology and follows best practices in ontology engineering, such as not allowing multiple inheritance of classes, using a predefined set of relations and usinga top level ontology.

Grace L Samson , Aminat Showole

Spatial data mining is the quantitative study of phenomena that are located in space. This paper investigates methods of mining patterns of a complex spatial data set (which generally describes any kind of data where the location in space of object holds importance). We based this research on the analysis of some spatial characteristics of certain objects. We began with describing the spatial pattern of events or objects with respect to their attributes; we looked at how to describe the spatial nature/characteristics of entities in an environment with respect to their spatial and non-spatial attributes. We also looked at modelling (predictive modelling/knowledge management of complex spatial systems), querying and implementing a complex spatial database (using data structure and algorithms). Critically speaking, the presence of spatial auto-correlation and the fact that continuous data types are always present in spatial data makes it important to create methods, tools and algorithms to mine spatial patterns in a complex spatial data set. This work is particularly useful to researchers in the ¯eld of data mining as it contributes a whole lot of knowledge to di®erent application areas of data mining especially spatial data mining. It can also be useful in teaching and likewise for other study purposes.

Abstract In recent years more interest of the data mining research community has been deserved in the topic of constrained-based mining because it increases the relevance of the result-set, reduces its volume and the amount of computational work. However, constrained-based mining will be completely feasible only when e cient optimizers for mining languages will be conceived and available. This paper is a rst step towards the construction of optimizers for a constraint-based mining language.

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Data Analytics in e-Learning: Approaches and Applications pp 21–42 Cite as

Public Datasets and Data Sources for Educational Data Mining

• P. S. Popescu   ORCID: orcid.org/0000-0003-4504-6144 4 ,
• M. C. Mihăescu 4 &
• M. L. Mocanu 4  
• First Online: 23 March 2022

496 Accesses

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 220))

Datasets are the starting point for any Machine Learning or Data mining workflow, and their impact on the overall performance of the whole system is vast. The data sources offer a variety of data that can be used directly or needs more or less preprocessing to produce a suitable dataset, but the main problem is what data is available and what data should be used to solve a specific task. The datasets explored in this chapter focus on the educational area and can directly impact any educational environment: online or just classical full-time education. This chapter aims to clarify what public datasets we have at this point, for what tasks are suitable, and presents a use case for a specific dataset in detail. The dataset referred deeply in this chapter was recently produced and includes attributes that can easily be mined from any e-Learning platform, so it can be a good baseline for anyone who starts in Educational Data Mining or tries to perform sample experiments in order to get a better insight before implementing a workflow in their system.

• Machine learning
• Educational data mining
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B.D.E, https://www.upenn.edu/learninganalytics/ryanbaker/bigdataeducation.html .

IEEEXplore, https://ieeexplore.ieee.org/Xplore/home.jsp .

Elsevier Science Direct, https://www.sciencedirect.com .

Research Gate, https://www.researchgate.net .

Data Collection Project, https://bluej.org/blackbox/ .

UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/index.php .

University Data Set, https://archive.ics.uci.edu/ml/datasets/University .

Data Set, https://archive.ics.uci.edu/ml/datasets/Teaching+Assistant+Evaluation .

Student Performance Data Set, http://archive.ics.uci.edu/ml/datasets/Student+Performance .

Student Alcohol Consumption, https://www.kaggle.com/uciml/student-alcohol-consumption .

User Knowledge, http://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling .

Educational Process Mining (EPM), https://tinyurl.com/y27yduo3 .

Open University Learning Analytics dataset, https://tinyurl.com/y6ysfant .

S.A.P., https://archive.ics.uci.edu/ml/datasets/Student+Academics+Performance .

Mendeley Data Repository, https://data.mendeley.com .

MOOC lectures dataset, https://data.mendeley.com/datasets/xknjp8pxbj/1 .

Dataset, https://data.mendeley.com/datasets/68mt8gms4j/2 .

Dataset, https://data.mendeley.com/datasets/6jmv43nffk/2 .

Dataset, https://data.mendeley.com/datasets/83tcx8psxv/1 .

KEEL, http://www.keel.es/

Harvard Dataverse, https://dataverse.harvard.edu .

HarvardX Person-Course Academic Year 2013, https://doi.org/10.7910/DVN/26147 .

Canvas Network Person-Course, https://doi.org/10.7910/DVN/1XORAL .

(MOOC-Ed), https://doi.org/10.7910/DVN/ZZH3UB .

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Popescu, P.S., Mihăescu, M.C., Mocanu, M.L. (2022). Public Datasets and Data Sources for Educational Data Mining. In: Mihăescu, M.C. (eds) Data Analytics in e-Learning: Approaches and Applications. Intelligent Systems Reference Library, vol 220. Springer, Cham. https://doi.org/10.1007/978-3-030-96644-7_2

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