As the world is getting more tech savvy and advancements made in the information technology especially in the healthcare industry has opened areas in data mining and machine learning. Within the area of data mining one technique which has gained a lot of popularity as well as skepticism among the auditors and fraud detectives is Benford’s Law or “The Law of First digit. In the past some researchers in Canada used the Benford’s Law distribution to detect anomalies within the claims amount data for one of the healthcare organization. In this article we will understand the mechanics of this technique and will also look at its practical usage on some random claims amount data.
Previous knowledge of forecasting is not required, but the reader should be familiar with basic data analysis and statistics (e.g., averages, correlation). To follow the example, the reader should also be familiar with R syntax. R packages needed: forecast, tseries, ggplot2.
In part one of this tutorial I discussed the use of R code to produce 3d scatterplots. This is a useful way to produce visual results of multi- variate linear regression models. While visual displays using scatterplots is a useful tool when using most datasets it becomes much more of a challenge when analyzing big data. These types of databases can contain tens of thousands or even millions of cases and hundreds of variables.
This document is intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machinelearned model, then you have the necessary background to read this document.
If you’re relatively new to the NLP and Text Analysis world, you’ll more than likely have come across some pretty technical terms and acronyms, that are challenging to get your head around, especially, if you’re relying on scientific definitions for a plain and simple explanation. We decided to put together a list of 10 common terms in Natural Language Processing which we’ve broken down in layman terms, making them easier to understand. So if you don’t know your “Bag of Words” from your LDA we’ve got you covered. The terms we chose were based on terms we often find ourselves explaining to users and customers on a day to day basis.
Taxonomies are known to provide a systematic and theoretical classification of elements in a particular domain and could be efficiently used to express concepts in a structural manner. Unfortunately, security literature witnesses a few taxonomies having about 40 nodes on average in mostly a narrowed scope and maximum of 25 nodes on mobile scope only. This study surveyed security related taxonomies with quality criteria and proposes new comprehensive mobile security taxonomy and mobile malware analysis subtaxonomy from not only defensive but also offensive point of view. We have developed a levelling scheme and notation for security taxonomies in general and proposed a new definite method to build the taxonomies having over 1,300 nodes. We have also visualized our taxonomies for researchers, security professionals and even common end users to provide comprehensible, well structured, and handy maps. As security threats and vulnerabilities dynamically increase and diversify, these new taxonomies would help to see the entire perspective of mobile security without losing any details and present new perspective to bring mobile computing and cyber security disciplines closer.
This is the third article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll provide an overview of the rise of IoT analytics.