scholarly journals Improving a state‐of‐the‐art heuristic for the minimum latency problem with data mining

2020 ◽  
Author(s):  
Ítalo Santana ◽  
Alexandre Plastino ◽  
Isabel Rosseti
Keyword(s):  
Data Mining ◽  
State Of The Art ◽  
Minimum Latency ◽  
Minimum Latency Problem

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

scholarly journals Orthros: A Low-Latency PRF

2021 ◽  
pp. 37-77
Author(s):  
Subhadeep Banik ◽  
Takanori Isobe ◽  
Fukang Liu ◽  
Kazuhiko Minematsu ◽  
Kosei Sakamoto
Keyword(s):  
Hardware Implementation ◽  
Block Cipher ◽  
State Of The Art ◽  
Security Analysis ◽  
Parallel Structure ◽  
Low Latency ◽  
Round Function ◽  
Minimum Latency ◽  
Primary Focus ◽  
Pseudorandom Function

We present Orthros, a 128-bit block pseudorandom function. It is designed with primary focus on latency of fully unrolled circuits. For this purpose, we adopt a parallel structure comprising two keyed permutations. The round function of each permutation is similar to Midori, a low-energy block cipher, however we thoroughly revise it to reduce latency, and introduce different rounds to significantly improve cryptographic strength in a small number of rounds. We provide a comprehensive, dedicated security analysis. For hardware implementation, Orthros achieves the lowest latency among the state-of-the-art low-latency primitives. For example, using the STM 90nm library, Orthros achieves a minimum latency of around 2.4 ns, while other constructions like PRINCE, Midori-128 and QARMA9-128- σ0 achieve 2.56 ns, 4.10 ns, 4.38 ns respectively.

AN INFORMATION-THEORETIC FILTER METHOD FOR FEATURE WEIGHTING IN NAIVE BAYES

2014 ◽  
Vol 28 (05) ◽  
pp. 1451007 ◽  
Author(s):  
CHANG-HWAN LEE
Keyword(s):  
Data Mining ◽  
Bayesian Learning ◽  
State Of The Art ◽  
Feature Weighting ◽  
New Method ◽  
Filter Method ◽  
Information Theoretic ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Unrealistic Assumption

In spite of its simplicity, naive Bayesian learning has been widely used in many data mining applications. However, the unrealistic assumption that all features are equally important negatively impacts the performance of naive Bayesian learning. In this paper, we propose a new method that uses a Kullback–Leibler measure to calculate the weights of the features analyzed in naive Bayesian learning. Its performance is compared to that of other state-of-the-art methods over a number of datasets.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 217-235 ◽  
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 1274-1292
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Image Features from Morphological Scale-Spaces

2011 ◽  
pp. 32-79
Author(s):  
Sébastien Lefèvre
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Video Processing ◽  
State Of The Art ◽  
Image Features ◽  
Video Data ◽  
Multimedia Data ◽  
Multimedia Data Mining ◽  
Shot Detection ◽  
Scale Spaces

Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data available. The aim of this chapter is to introduce researchers, especially new ones, to the “video representation, processing, and segmentation techniques”. This includes an easy and smooth introduction, followed by principles of video structure and representation, and then a state-of-the-art of the segmentation techniques focusing on the shot-detection. Performance evaluation and common issues are also discussed before concluding the chapter.

A multi-improvement local search using dataflow and GPU to solve the minimum latency problem

2020 ◽  
Vol 98 ◽  
pp. 102661 ◽  
Author(s):  
Rodolfo Pereira Araujo ◽  
Igor Machado Coelho ◽  
Leandro Augusto Justen Marzulo
Keyword(s):  
Local Search ◽  
Minimum Latency ◽  
Minimum Latency Problem
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