If you're involved in the tech world, you'll know that data mining has been creating a buzz for years. But, how exactly is it done? And what tools do data engineers actually use to'mine' useful information from large databases? The main tools in a data miner's arsenal are algorithms. Today, I'm going to look at the top 10 data mining algorithms, and make a comparison of how they work and what each can be used for. Algorithms are a set of instructions that a computer can run.

About this course: Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity.

A few weeks ago, I wrote about how and why I was learning Machine Learning, mainly through Andrew Ng's Coursera course. Machine Learning is built on prerequisites, so much so that learning by first principles seems overwhelming. Do you really need to spend a month learning linear algebra? You'll be okay if you have some math and programming experience. You really just have to be familiar with Sigma notation and be able to express it in a for loop. Sure, your assignments will take longer to complete and the first few times you see those giant equations your head will spin, but you can do this! Calculus is not even required.

Textual queries in networks such as Twitter can have more than one label resulting in a multi-label classification problem. To reduce computational costs a low-dimensional representation of a large network is learned that preserves proximity among nodes in the same community. Similar to sequence of words in a sentence, DeepWalk consider sequence of nodes in a shallow graph and clustering is done using hierarchical softmax in an unsupervised manner. In this paper, we generate network abstractions at different levels using deep convolutional neural networks. Since, class labels of connected nodes in a network keep changing we consider a fuzzy recurrent feedback controller to ensure robustness to noise.

Anomalies in healthcare claims data can be indicative of possible fraudulent activities, contributing to a significant portion of overall healthcare costs. Medicare is a large government run healthcare program that serves the needs of the elderly in the United States. The increasing elderly population and their reliance on the Medicare program create an environment with rising costs and increased risk of fraud. The detection of these potentially fraudulent activities can recover costs and lessen the overall impact of fraud on the Medicare program. In this paper, we propose a new method to detect fraud by discovering outliers, or anomalies, in payments made to Medicare providers. We employ a multivariate outlier detection method split into two parts. In the first part, we create a multivariate regression model and generate corresponding residuals. In the second part, these residuals are used as inputs into a generalizable univariate probability model. We create this Bayesian probability model using probabilistic programming. Our results indicate our model is robust and less dependent on underlying data distributions, versus Mahalanobis distance. Moreover, we are able to demonstrate successful anomaly detection, within Medicare specialties, providing meaningful results for further investigation.

In this paper, we investigate the authorship of several short historical texts that are written by ten ancient Arabic travelers: this Arabic dataset, which was collected by the authors in 2011, and called AAAT (Authorship attribution of Ancient Arabic Texts) corpus, is considered as a reference dataset in Arabic. Several experiments of authorship attribution are conducted by using different features namely: characters, character n-grams, and lexical features such as words, word n-grams, and rare words. On the other hand, different classifiers are employed, such as: statistical distances, Multi Layer Percep-tron (MLP), Support Vector Machines (SVM) and Linear Regression (LR). In this investigation, a new fusion technique is proposed to enhance the overall performances of the classifiers: it is called Score Based Fusion (SBF). Results show good attribution performances with an optimal score between 80% and 90% of good authorship attribution. The proposed fusion technique raised this score to 100% of good authorship attribution. Moreover, this comparative survey has revealed interesting results concerning the Arabic language and more particularly with short texts.

Word Sense Disambiguation (WSD) is a classification task that consists of determining which of the senses of an ambiguous word is activated in a specific context. Research in this field has primarily concentrated on investigating English and a few other well-resourced languages. Recently, studies done on a corpus of Old English (Wunderlich 2015) showed that, even with limited resources, it is still possible to approach the problem of WSD. In this paper, a WSD system has been developed for the Low Resource Language (LRL) Venetan, which has recently received some attention from the Natural Language Processing (NLP) community. Our main contributions are twofold: first, we select and annotate a corpus for Venetan, considering two words (one abstract and one concrete term) and using two levels of annotation (fine- and coarse-grained), reporting on annotator agreement. Second, we report results of proof-of-concept experiments of supervised WSD performed with Support Vector Machines on this corpus. To our knowledge, our work is the first time that WSD for a European Dialect like Venetan has been studied.

The multilabel learning problem with large number of labels, features, and data-points has generated a tremendous interest recently. A recurring theme of these problems is that only a few labels are active in any given data point as compared to the total number of labels. However, only a small number of existing work take direct advantage of this inherent extreme sparsity in the label space. By the virtue of Restricted Isometry Property (RIP), satisfied by many random ensembles, we propose a novel procedure for multilabel learning known as RIPML. During the training phase, in RIPML, labels are projected onto a random low-dimensional subspace followed by solving a least-square problem in this subspace. Inference is done by a k-nearest neighbor (kNN) based approach. We demonstrate the effectiveness of RIPML by conducting extensive simulations and comparing results with the state-of-the-art linear dimensionality reduction based approaches.

Demand forecasting is an important challenge for industries seeking to optimize service quality and expenditures. Generating accurate forecasts is difficult because it depends on the quality of the data available to train predictive models, as well as on the model chosen for the task. We evaluate the approach on two datasets of varying complexity and compare the results with three machine learning algorithms. Results show our approach can outperform these approaches.

Millions of users use online, open-ended blocks programming environments like App Inventor to learn how to program and to build personally meaningful programs and apps. As part of understanding the computational thinking concepts being learned by these users, we want to distinguish original projects that they create from unoriginal ones that arise from learning activities like tutorials and exercises. Given all the projects of students taking an App Inventor course, we describe how to automatically classify them as original vs. unoriginal using a hierarchical clustering technique. Although our current analysis focuses only on a small group of users (16 students taking a course in our institution) and their 902 projects, our findings establish a foundation for extending this analysis to larger groups of users.