Kernel Clusterand SVMs-Based Algorithm for Iris Rough Classification in Massive Databases

2008 ◽  
Author(s):  
Zheng Tao ◽  
Xie Mei
Keyword(s):  
Massive Databases

The Quantification of Law: Counting, Predicting, and Valuating

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Rafael Viana Ribeiro
Keyword(s):  
Legal Reasoning ◽  
Public Institutions ◽  
Private Companies ◽  
Public And Private ◽  
Legal Professionals ◽  
The Public ◽  
Past Behavior ◽  
Legal Arguments ◽  
Institutional Actors ◽  
Massive Databases

Legal reasoning is increasingly quantified. Developers in the market and public institutions in the legal system are making use of massive databases of court opinions and other legal communications to craft algorithms to assess the effectiveness of legal arguments or predict court judgments; tasks that were once seen as the exclusive province of seasoned lawyers’ obscure knowledge. New legal technologies promise to search heaps of documents for useful evidence, and to analyze dozens of factors to quantify a lawsuit’s odds of success. Legal quantification initiatives depend on the availability of reliable data about the past behavior of courts that institutional actors have attempted to control. The development of initiatives in legal quantification is visible as public bodies craft their own tools for internal use and access by the public, and private companies create new ways to valorize the “raw data” provided by courts and lawyers by generating information useful to the strategies of legal professionals, as well as to the investors that re-valorize legal activity by securitizing legal risk through litigation funding.

2016

2019 ◽  
pp. 179-210
Author(s):  
Jennifer Stromer-Galley
Keyword(s):  
Decision Making ◽  
Digital Media ◽  
Data Driven ◽  
The Public ◽  
Voter Information ◽  
Massive Databases ◽  
Get Out The Vote

The quest for data-driven campaigning in 2012—creating massive databases of voter information for more effective micro-targeting—found greater efficacy and new controversy in 2016. The Trump campaign capitalized on the power of digital advertising to reach the public to engage in unprecedented mass-targeted campaigning. His campaign spent substantially more on Facebook and other digital media paid ads than Clinton. Yet, the company that Trump worked with, Cambridge Analytica, closed up shop in 2018 under a cloud of controversy about corrupt officials and voter manipulation in several countries, as well as ill-begotten data of Facebook users that drove their micro-targeting practices. The Clinton campaign modeled itself on data-driven successes of the Obama campaign, yet the algorithms that drove their decision making were flawed, thereby leading her campaign to underperform in essential swing states. Similar to the Romney campaign’s Narwhal challenges on Election Day when the campaign effectively was flying blind on get-out-the-vote numbers, the Clinton plane was flying on bad coordinates, ultimately causing her campaign to crash in critical swing states.

The HGEDA Hybrid Algorithm for OLAP Data Cubes

2011 ◽  
Vol 130-134 ◽  
pp. 3158-3162
Author(s):  
Chen Jia ◽  
Hong Wei Chen
Keyword(s):  
Hybrid Algorithm ◽  
New Approach ◽  
Useful Knowledge ◽  
Estimation Of Distribution ◽  
On Line ◽  
Analytical Processing ◽  
Distribution Algorithms ◽  
Problem Instances ◽  
Original Objective ◽  
Massive Databases

On-Line Analytical Processing (OLAP) tools are frequently used in business, science and health to extract useful knowledge from massive databases. An important and hard optimization problem in OLAP data warehouses is the view selection problem, consisting of selecting a set of aggregate views of the data for speeding up future query processing. In this paper we present a new approach, named HGEDA, which is a new hybrid algorithm based on genetic and estimation of distribution algorithms. The original objective is to get benefits from both approaches. Experimental results show that the HGEDA are competitive with the genetic algorithm on a variety of problem instances, often finding approximate optimal solutions in a reasonable amount of time.

An Intuitionistic Fuzzy Approach With Rough Entropy Measure to Detect Outliers in Two Universal Sets

2020 ◽  
Vol 9 (3) ◽  
pp. 100-117
Author(s):  
Sangeetha T. ◽  
Geetha Mary A.
Keyword(s):  
Outlier Detection ◽  
Categorical Data ◽  
Numerical Data ◽  
Entropy Measure ◽  
Weighted Density ◽  
Intuitionistic Fuzzy ◽  
Decision Attributes ◽  
Density Values ◽  
Class Labels ◽  
Massive Databases

The process of recognizing patterns, collecting knowledge from massive databases is called data mining. An object which does not obey and deviates from other objects by their characteristics or behavior are known as outliers. Research works carried out so far on outlier detection were focused only on numerical data, categorical data, and in single universal sets. The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values. The proposed method, weighted density outlier detection, is based on rough entropy, and is employed to detect outliers. Since it is unsupervised, without considering class labels of decision attributes, weighted density values for all conditional attributes and objects are calculated to detect outliers. For experimental analysis, the Iris dataset from the UCI repository is taken to detect outliers, and comparisons have been made with existing algorithms to prove its efficiency.

A dynamic knowledge base and its data compression with homomorphism

2021 ◽  
pp. 1-11
Author(s):  
Bin Qin
Keyword(s):  
Information Systems ◽  
Data Compression ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Incremental Algorithm ◽  
Mathematical Tool ◽  
Incremental Algorithms ◽  
Knowledge Reduction ◽  
New Information ◽  
Massive Databases

In reality there are always a large number of complex massive databases. The notion of homomorphism may be a mathematical tool for studying data compression in knowledge bases. This paper investigates a knowledge base in dynamic environments and its data compression with homomorphism, where “dynamic” refers to the fact that the involved information systems need to be updated with time due to the inflow of new information. First, the relationships among knowledge bases, information systems and relation information systems are illustrated. Next, the idea of non-incremental algorithm for data compression with homomorphism and the concept of dynamic knowledge base are introduced. Two incremental algorithms for data compression with homomorphism in dynamic knowledge bases are presented. Finally, an experimental analysis is employed to demonstrate the applications of the non-incremental algorithm and the incremental algorithms for data compression when calculating the knowledge reduction of dynamic knowledge bases.

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