Data Mining and Machine learning are areas that have been influenced by each other, although they have many common things, yet they have different ends.

Data Mining is performed on certain data sets by humans to find interesting patterns between the items in the data set. Data Mining uses techniques created by machine learning for predicting the results while machine learning is the capability of the computer to learn from a minded data set.

Machine learning algorithms take the information that represents the relationship between items in data sets and creates models in order to predict future results. These models are nothing more than actions that will be taken by the machine to achieve a result.

https://www.javatpoint.com/data-mining-vs-machine-learning

Data Mining vs Machine Learning

An extensive exploration of data mining: https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat06466.pub2

Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets.
https://www.ibm.com/cloud/learn/data-mining

University of Michigan - Ann Arbor

The list of helpful books about data science:
1. Data science and prediction

Helpful data science research papers

A paper on biases in supervised learning : [arxiv.org/abs/1610.02413](https://arxiv.org/abs/1610.02413) 

Equality of opportunnity in Supervised Learning

 A different approach to reducing implicit bias :
 [papers.ssrn.com/sol3/papers.cfm?abstract_id=3024938](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3024938) 

How copyright law can fix AI's implicit bias problem

Men also like shopping : reducing gender bias amplification using corpus level constraints : [arxiv.org/abs/1707.09457](https://arxiv.org/abs/1707.09457) 

Interesting paper on reducing biases

An interesting paper I read recently.
Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications : 
[arxiv.org/abs/1902.03825](https://arxiv.org/abs/1902.03825) 


As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M 1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree are analyzed on this data set. Keywords- Data Mining, Classification Techniques, UCI repository, Breast Cancer, Classification Algorithms

Applications paper

Baudat, G, & Anouar, F. (1/10/2000). Generalized Discriminant Analysis Using a Kernel Approach. Neural Computation, 12(10), 2385–2404. Article, CAMBRIDGE: MIT Press.

Generalized Discriminant Analysis Using a Kernel Approach

Daniel, Ben Kei. “Big Data and Data Science: A Critical Review of Issues for Educational Research.” British Journal of Educational Technology, vol. 50, no. 1, Wiley Subscription Services, Inc, 1/2019, pp. 101–13, doi:10.1111/bjet.12595.

https://search.lib.umich.edu/articles/record/FETCH-LOGICAL-12895-e928cb165ac232e2cee04bd3748d550340ce07aebdc5f6b72a2ccca9e949d2453

Big Data and data science: A critical review of issues for educational research

A paper that reinforces the need for "efficiency" as an evaluation criteria in AI research & making AI more inclusive. https://arxiv.org/abs/1907.10597

Paper on Green AI

A helpful paper on how data is used in the real world https://www.liebertpub.com/doi/full/10.1089/big.2013.1508

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot—even “sexy”—career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

Data Science and its Relationship to Big Data and Data-Driven Decision Making

Data Mining

This paper introduce an approach to collaborative filtering that can give Google News users personalized recommendation.

Das, Abhinandan & Datar, Mayur & Garg, Ashutosh & Rajaram, ShyamSundar. (2007). Google news personalization: Scalable online collaborative filtering. 16th International World Wide Web Conference, WWW2007. 271-280. 10.1145/1242572.1242610. Retrieve from https://www.researchgate.net/publication/221023652_Google_news_personalization_Scalable_online_collaborative_filtering

Several approaches to collaborative filtering have been stud- ied but seldom have studies been reported for large (several million users and items) and dynamic (the underlying item set is continually changing) settings. In this paper we de- scribe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collabo- rative filtering using MinHash clustering, Probabilistic La- tent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and con- sequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.

Google News Personalization: Scalable Online Collaborative Filtering

As for the COVID-19 situation, this article tells us an important application of data science.
Brossette, Stephen & Sprague, Alan & Hardin, James & Waites, Ken & Jones, Warren & Moser, Stephen. (1998). Association Rules and Data Mining in Hospital Infection Control and Public Health Surveillance. Journal of the American Medical Informatics Association : JAMIA. 5. 373-81. 10.1136/jamia.1998.0050373. Retrieve from https://www.researchgate.net/publication/13615076_Association_Rules_and_Data_Mining_in_Hospital_Infection_Control_and_Public_Health_Surveillance

The authors consider the problem of identifying new, unexpected, and interesting patterns in hospital infection control and public health surveillance data and present a new data analysis process and system based on association rules to address this problem.
The authors first illustrate the need for automated pattern discovery and data mining in hospital infection control and public health surveillance. Next, they define association rules, explain how those rules can be used in surveillance, and present a novel process and system--the Data Mining Surveillance System (DMSS)--that utilize association rules to identify new and interesting patterns in surveillance data.
Experimental results were obtained using DMSS to analyze Pseudomonas aeruginosa infection control data collected over one year (1996) at University of Alabama at Birmingham Hospital. Experiments using one-, three-, and six-month time partitions yielded 34, 57, and 28 statistically significant events, respectively. Although not all statistically significant events are clinically significant, a subset of events generated in each analysis indicated potentially significant shifts in the occurrence of infection or antimicrobial resistance patterns of P. aeruginosa.
The new process and system are efficient and effective in identifying new, unexpected, and interesting patterns in surveillance data. The clinical relevance and utility of this process await the results of prospective studies currently in progress.

Application in Hospital Infection Control and Public Health Surveillance

Association rule has many related techniques. In this research paper, it is used for assessing the risk of diabetes mellitus. 

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6514877&isnumber=6974945

G. J. Simon, P. J. Caraballo, T. M. Therneau, S. S. Cha, M. R. Castro and P. W. Li, "Extending Association Rule Summarization Techniques to Assess Risk of Diabetes Mellitus," in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 1, pp. 130-141, Jan. 2015, doi: 10.1109/TKDE.2013.76.
Abstract: Early detection of patients with elevated risk of developing diabetes mellitus is critical to the improved prevention and overall clinical management of these patients. We aim to apply association rule mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. Given the high dimensionality of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. We proposed extensions to incorporate risk of diabetes into the process of finding an optimal summary. We evaluated these modified techniques on a real-world prediabetic patient cohort. We found that all four methods produced summaries that described subpopulations at high risk of diabetes with each method having its clear strength. For our purpose, our extension to the Buttom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.

Applications in Diabetes Mellitus

Liben-nowell, David & Kleinberg, Jon. (2003). The Link Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology. 58. 10.1002/asi.20591. Retrieved from https://www.cs.cornell.edu/home/kleinber/link-pred.pdf


Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures for analyzing the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.

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