DOI: 10.18713/JIMIS-010917-3-1 Submitted: 12/2/2016 - Published: 6/2/2017 Volume: 3 - Year: 2017 Issue: Digital Contextualization Editors: Frédéric Lebaron, Brigitte Le Roux, Fionn Murtagh, Evelyn Ruppert The relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources; dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case studies are both experimented with, and are reviewed. In such aspects as paradigms followed, and technical implementation, implicitly and explicitly, an important point made is the major relevance of such work for both burgeoning analytical needs and for new analytical areas including Big Data analytics, and so on. For the general reader, it is aimed to display and describe, first of all, the analytical outcomes that are subject to analysis here, and then proceed to detail the more quantitative outcomes that fully support the analytics carried out.

Here is our selection of featured articles and resources posted since Monday. We had numerous high quality articles submitted this week!

In spite of the rapid development in data acquisition technology resulting in the explosive collection of acquired datasets, techniques such as data organization and classification, manipulation, and analysis of very large, diverse, heterogeneous datasets have only evolved modestly. This has led to hindrances in effective utility and better understanding of the acquired, large-scale data for knowledge discovery. In an industrial setting, an interesting visual from McKinsey illustrates that despite collecting data from tens of thousands of sensors, less than 1% is actually utilized. Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Typically, homogeneous data objects, i.e. data objects having the same data type, are grouped together using some of the well-known clustering algorithms.

This is a guest repost by Jacob Joseph. An Outlier is an observation or point that is distant from other observations/points. But, how would you quantify the distance of an observation from other observations to qualify it as an outlier. Outliers are also referred to as observations whose probability to occur is low. But, again, what constitutes low??

Most of the beginners start by learning regression. It is simple to learn and use, but does that solve our purpose? Because, you can do so much more than just Regression! Think of machine learning algorithms as an armory packed with axes, sword, blades, bow, dagger etc. You have various tools, but you ought to learn to use them at the right time.

The following problem appeared as an assignment in the coursera course Algorithm-I by Prof.Robert Sedgewick from the Princeton University few years back (and also in the course cos226 offered at Princeton). The problem definition and the description is taken from the course website and lectures. The original assignment was to be done in java, where in this article both the java and a corresponding python implementation will also be described. The idea is to build a BST with points in the nodes, using the x– and y-coordinates of the points as keys in strictly alternating sequence, starting with the x-coordinates, as shown in the next figure. The following figures and animations show how the 2-d-tree is grown with recursive space-partioning for a few sample datasets.

In the syntax below, we are asking Python to import numpy and pandas package. The'as' is used to alias package name.

Assessing heterogeneous treatment effects has become a growing interest in advancing precision medicine. Individualized treatment effects (ITE) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees (Su et al., 2009). To this end, we first propose a smooth sigmoid surrogate (SSS) method, as an alternative to greedy search, to speed up tree construction. RFIT outperforms the traditional `separate regression' approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT can be obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial.

Many machine learning and artificial intelligence tasks involve the separation of a data matrix into a low-rank structure and a sparse part capturing different information. Robust principal component analysis (RPCA) Candes et al. [2011], Chandrasekaran et al. [2011] is a popular framework that logically characterizes this matrix separation problem. Nevertheless, prior side information, oftentimes in the form of features, may also be present in practice. For instance, features are available for the following tasks: - Collaborative filtering: apart from ratings of an item by other users, the profile of the user and the description of the item can also be exploited in making recommendations Chiang et al. [2015]; - Relationship prediction: user behaviours and message exchanges can assist in finding missing links on social media networks Xu et al. [2013]; - Person-specific facial deformable models: an orthonormal subspace learnt from manually annotated data captured in-the-wild, when fed into an im-1 age congealing procedure, can help produce more correct fittings Sagonas et al. [2014]. It is thus reasonable to investigate how propitious it is for RPCA to exploit the available features. Indeed, recent results Liu et al. [2017] indicate that features are not redundant at all. In the setting of multiple subspaces, RPCA degrades as the number of subspaces grows because of the increased row-coherence. On the other hand, the use of feature dictionaries allows accurate low-rank recovery by removing the dependency on row-coherence.

In this paper, we consider the interpretability of the foundational Laplacian-based semi-supervised learning approaches on graphs. We introduce a novel flow-based learning framework that subsumes the foundational approaches and additionally provides a detailed, transparent, and easily understood expression of the learning process in terms of graph flows. As a result, one can visualize and interactively explore the precise subgraph along which the information from labeled nodes flows to an unlabeled node of interest. Surprisingly, the proposed framework avoids trading accuracy for interpretability, but in fact leads to improved prediction accuracy, which is supported both by theoretical considerations and empirical results. The flow-based framework guarantees the maximum principle by construction and can handle directed graphs in an out-of-the-box manner.