Data Mining - Concepts and Techniques.

History of tuberculosis

Lehrbuch der Anthropologie in systematischer Darstellung: mit besonderer Berücksichtigung der anthropologischen Methoden ; für Studierende, Ärzte und Forschungsreisende ; mit 460 Abbildungen im Text, 3 Tafeln und 2 Beobachtungsblättern

Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

/pdf/catboost-for-big-data-an-interdisciplinary-review-utp91h5i0y.pdf

CatBoost for big data: an interdisciplinary review

https://www.hse.ru/data/2010/11/01/1223500185/InformationSciences.pdf

Mining gene expression data with pattern structures in formal concept analysis

We describe here a general approach for rare itemset mining. While mining literature has been almost exclusively focused on frequent itemsets, in many practical situations rare ones are of higher interest (e.g., in medical databases, rare combinations of symptoms might provide useful insights for the physicians). Based on an examination of the relevant substructures of the mining space, our approach splits the rare itemset mining task into two steps, i.e., frequent itemset part traversal and rare itemset listing. We propose two algorithms for step one, a naive and an optimized one, respectively, and another algorithm for step two. We also provide some empirical evidence about the performance gains due to the optimized traversal.

/pdf/towards-rare-itemset-mining-4f97w9x79k.pdf

Towards Rare Itemset Mining

In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment.

/pdf/case-base-mining-for-adaptation-knowledge-acquisition-7o79rlfd2v.pdf

Case base mining for adaptation knowledge acquisition

With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

/pdf/performance-analysis-of-different-types-of-machine-learning-1zpmo1mkgg.pdf

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

In this paper, we present and detail a multifunctional itemset mining algorithm called Zart, which is based on the Pascal algorithm. Zart shows a number of additional features and performs the following, usually independent, tasks: identify frequent closed itemsets and associate generators to their closures. This makes Zart a complete algorithm for computing classes of itemsets including generators and closed itemsets. These characteristics allow one to extract minimal non-redundant association rules, a useful and lossless representation of association rules. In addition, being based on the Pascal algorithm, Zart has a rather efficient behavior on weakly and strongly correlated data. Accordingly, Zart is at the heart of the Coron platform, which is a domain independent, multi-purposed data mining platform, incorporating a rich collection of data mining algorithms.

/pdf/zart-a-multifunctional-itemset-mining-algorithm-2iq5j3figm.pdf

ZART: A Multifunctional Itemset Mining Algorithm

CORON is a framework for levelwise algorithms that are designed to find frequent and/or frequent closed itemsets in binary contexts. Datasets can be very different in size, number of objects, number of attributes, density, etc. As there is no one best algorithm for arbitrary datasets, we want to give a possibility for users to try different algorithms and choose the one that best suits their needs.

https://hal.inria.fr/inria-00001201/document

Laszlo Szathmary

Papers

Towards Rare Itemset Mining

Case base mining for adaptation knowledge acquisition

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

ZART: A Multifunctional Itemset Mining Algorithm

CORON: A Framework for Levelwise Itemset Mining Algorithms