Association Learning


101 ML Algorithms

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The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization. In this post, you'll find 101 machine learning algorithms, including useful cheat sheets to help you know when to use each one (if available). At Data Science Dojo, our mission is to make data science (machine learning in this case) available to everyone. Whether you join our data science bootcamp, read our blog, or watch our tutorials, we want everyone to have the opportunity to learn data science. Having said that, each accordion dropdown is embeddable if you want to take them with you.


Understanding Association Rule Learning & Its Role In Data Mining

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Data Mining enables users to analyse, classify and discover correlations among data. One of the crucial tasks of this process is Association Rule Learning. An important part of data mining is anomaly detection, which is a procedure of search for items or events that do not correspond to a familiar pattern. These familiar patterns are termed anomalies and interpret critical and actionable data in various application fields. This concept can be best understood with the supermarket example.


MachineX: Understanding FP-Tree Construction - DZone AI

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In my previous blog, MachineX: Why No One Uses an Apriori Algorithm for Association Rule Learning, we discussed one of the first algorithms in association rule learning, Apriori algorithm. Although, even after being so simple and clear, it has some weaknesses as discussed in the above-mentioned blog. A significant improvement over the Apriori algorithm is the FP-Growth algorithm. To understand how the FP-Growth algorithm helps in finding frequent items, we first have to understand the data structure used by it to do so, the FP-Tree, which will be our focus in this blog. To put it simply, an FP-Tree is a compressed representation of the input data.



Can Machine Learning Correct Commonly Accepted Knowledge and Provide Understandable Knowledge in Care Support Domain? Tackling Cognitive Bias and Humanity from Machine Learning Perspective

AAAI Conferences

This paper focuses on care support knowledge (especially focuses on the sleep related knowledge) and tackles its cognitive bias and humanity aspects from machine learning perspective through discussion of whether machine learning can correct commonly accepted knowledge and provide understandable knowledge in care support domain. For this purpose, this paper starts by introducing our data mining method (based on association rule learning) that can provide only necessary number of understandable knowledge without probabilities even if its accuracy slightly becomes worse, and shows its effectiveness in care plans support systems for aged persons as one of healthcare systems. The experimental result indicates that (1) our method can extract a few simple knowledge as understandable knowledge that clarifies what kinds of activities (e.g., rehabilitation, bathing) in care house contribute to having a deep sleep, but (2) the apriori algorithm as one of major association rule learning methods is hard to provide such knowledge because it needs calculate all combinations of activities executed by aged persons.


More Data Mining with R Udemy

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In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response.


Lift (data mining) - Wikipedia

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In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. A targeting model is doing a good job if the response within the target is much better than the average for the population as a whole. Lift is simply the ratio of these values: target response divided by average response. For example, suppose a population has an average response rate of 5%, but a certain model (or rule) has identified a segment with a response rate of 20%. Then that segment would have a lift of 4.0 (20%/5%).


Exploring 250,000 Movies with Association Discovery

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Hot on the heels of our Fall 2015 Release webinar including our Association Discovery (aka Association Rule Learning) implementation, we wanted to give this new capability a spin on our blog in order to get our readers warmed up.


Mining Rules from Player Experience and Activity Data

AAAI Conferences

Feedback on player experience and behaviour can be invaluable to game designers, but there is need for specialised knowledge discovery tools to deal with high volume playtest data. We describe a study witha commercial third-person shooter, in which integrated player activity and experience data was captured and mined for design-relevant knowledge. We demonstrate that association rule learning and rule templates can be used to extractmeaningful rules relating player activity and experience during combat. We found that the number, type and quality of rules varies between experiences, and is affected by feature distributions. Further work is required on rule selection and evaluation.


Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets

Journal of Artificial Intelligence Research

This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of non-zero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worst-case bounds for this structure for several models of data distribution. We empirically demonstrate that tractably-sized data structures can be produced for large real-world datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its leaves. We show how the ADtree can be used to accelerate Bayes net structure finding algorithms, rule learning algorithms, and feature selection algorithms, and we provide a number of empirical results comparing ADtree methods against traditional direct counting approaches. We also discuss the possible uses of ADtrees in other machine learning methods, and discuss the merits of ADtrees in comparison with alternative representations such as kd-trees, R-trees and Frequent Sets.