There is no decision, except, Logistic Regression is parametric, while IDT is non-parametric. What you need to know is, they give you similar stuff you'll need, but using different approaches. AND, one is preferable over the other in certain situations. For eg, IDT can be very helpful when you want to know rules to create your segments! Also, when you have no clue what your data looks like, IDT is a good place to start.

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.

In recent years the e-sports environment around online digital games have gained immense momentum. SuperData [1] reported a worldwide audience of 71 million people who watch competitive gaming, with 31.4 million participation or viewership in the United States. On the company side, considerable resources are being allocated to support the e-sports environment from the main companies in the domain such as Riot Games, Wargaming, Valve, Ubisoft and Turbine. In 2013, prize money for the top three tournaments (Defense of the Ancients 2 (DotA 2), League of Legends (LoL) and Call of Duty (CoD) Championships) rose above 1 million USD. For DotA 2, the main tournament of the year, The International, contained a 10.9 million USD prize pool at the time of writing [2]. This is a tenfold increase in just 2 years and the largest in e-sports history. The prize increase was driven by an intiative by Valve, where players contributed to the prize pool by buying an in-game item The Compendium. In return, the compendium gave players additional ways to interact with the tournament (e.g.

The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 million data points, over 20,000 unique players and more than 650 products. Furthermore, an interactive visualization, based on Sankey flow diagrams, is presented which shows the proportion of the different clusters across each time bin, as well as the flow of players between clusters. The diagram allows evaluation of migration of players between clusters as a function of time, as well as churn analysis. The presented work provides a template analysis and visualization model for progression-based or temporal-based analysis of player behavior broadly applicable to games. Keywords: virtual economy, massively multi-player online game, game analytics, auction house, longitudinal analysis 1. Introduction Online games form a major component of the games industry, and have expanded strongly in terms of market share, variety and market penetration in recent years, notably due to the increasing availability of mobile platforms and the introduction of Free-to-Play (F2P) business models by the interactive entertainment industry [15,29,50,51]. Of the wide variety of online games, the Massively Multi-Player Online Game (MMOG) format, and its derivatives, is unique in that these games see thousands or more players interacting within the same virtual environment [21,22,42,46,64]. The games can support complex virtual societies that include ingame economies [3,8].

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.

We propose new metrics on sets, ontologies, and functions that can be used in various stages of probabilistic modeling, including exploratory data analysis, learning, inference, and result interpretation. These new functions unify and generalize some of the popular metrics on sets and functions, such as the Jaccard and bag distances on sets and Marczewski-Steinhaus distance on functions. We then introduce information-theoretic metrics on directed acyclic graphs drawn independently according to a fixed probability distribution and show how they can be used to calculate similarity between class labels for the objects with hierarchical output spaces (e.g., protein function). Finally, we provide evidence that the proposed metrics are useful by clustering species based solely on functional annotations available for subsets of their genes. The functional trees resemble evolutionary trees obtained by the phylogenetic analysis of their genomes.

Semidefinite programming has become a key optimization tool in many areas of applied mathematics, signal processing and machine learning. SDPs often arise naturally from the problem structure, or are derived as surrogate optimizations that are relaxations of difficult combinatorial problems [7, 1, 8]. In spite of the importance of SDPs in principle--promising efficient algorithms with polynomial runtime guarantees--it is widely recognized that current optimization algorithms based on interior point methods can handle only relatively small problems. Thus, a considerable gap exists between the theory and applicability of SDP formulations. Scalable algorithms for semidefinite programming, and closely related families of nonconvex programs more generally, are greatly needed. A parallel development is the surprising effectiveness of simple classical procedures such as gradient descent for large scale problems, as explored in the recent machine learning literature. In many areas of machine learning and signal processing such as classification, deep learning, and phase retrieval, gradient descent methods, in particular first order stochastic optimization, have led to remarkably efficient algorithms that can attack very large scale problems [3, 2, 10, 6]. In this paper we build on this work to develop first-order algorithms for solving the rank minimization problem under random measurements and a closely related family of semidefinite programs. Our algorithms are efficient and scalable, and we prove that they attain linear convergence to the global optimum under natural assumptions.

Model evaluation metrics are used to assess goodness of fit between model and data, to compare different models, in the context of model selection, and to predict how predictions (associated with a specific model and data set) are expected to be accurate. Confidence intervals are used to assess how reliable a statistical estimate is. Wide confidence intervals mean that your model is poor (and it is worth investigating other models), or that your data is very noisy if confidence intervals don't improve by changing the model (that is, testing a different theoretical statistical distribution for your observations.) Modern confidence intervals are model-free, data -driven: click here to see how to compute them. A more general framework to assess and reduce sources of variance is called analysis of variance.

It looks at cluster analysis as an analysis of variance problem. This method involves an agglomerative clustering algorithm. It starts out with n clusters of size 1 and continues until all the observations are included into one cluster. This method is most appropriate for quantitative variables, and not binary variables. Then you can set some threshold for the outlier clusters, like the size of that cluster is smaller then n*0.1%.

Machine learning is generating a tremendous amount of attention these days from the press as well as the practitioners. And rightly so – machine learning is a transformative technology. But despite the references to the topic, the money raised from venture capitalists, and the spotlight that Google is bringing to the subject, machine learning is still poorly understood outside of a core group of highly technical leaders. This has the effect of underestimating how transformative machine learning is going to be. It also has the effect of shielding business leaders from what they need to do to prepare for the era of machine learning.