Data Mining Algorithms: An Overview

Abstract

The research on data mining has successfully yielded numerous tools, algorithms, methods and approaches for handling large amounts of data for various purposeful use and  problem solving. Data mining has become an integral part of many application domains such as data ware housing, predictive analytics, business intelligence, bio-informatics and decision support systems. Prime objective of data mining is to effectively handle large scale data, extract actionable patterns, and gain insightful knowledge. Data mining is part and parcel of knowledge discovery in databases (KDD) process. Success and improved decision making normally depends on how quickly one can discover insights from data. These insights could be used to drive better actions which can be used in operational processes and even predict future behaviour. This paper presents an overview of various algorithms necessary for handling large data sets. These algorithms define various structures and methods implemented to handle big data. The review also discusses the general strengths and limitations of these algorithms. This paper can quickly guide or an eye opener to the data mining researchers on which algorithm(s) to select and apply in solving the problems they will be investigating.

Diabetes Risk Data Mining Method Based on Electronic Medical Record Analysis

“…The comparison with the threshold γ is used to determine whether the tree branches to the left or right [ 31 ]. At the node t of the tree, use the function p ( c|I , x ) to distinguish and store the feature c of the data set [ 32 , 33 ]. The average distribution of all trees in the forest gives the final classification result; The formula is as follows:: …”

“…e experiment included data on 3455 women who notified pregnancy between 1999 and 2012. e development of a diet-based diabetes risk score in the study aims to quantify the association between adherence to the prior diet score and the incidence of type 2 diabetes,; among the 9 food categories for which diet scores (reported to be negatively correlated with the incidence of type 2 diabetes), 3 food categories are reported to be directly related to type 2 diabetes. Donazar-Ezcurra M assessed three types of compliance with diet-based diabetes risk scores: low (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), intermediate (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39), and high (40-60). Compared with the lowest category, the higher category showed an independent inverse correlation with the risk of developing gestational diabetes (multivariate-adjusted OR 0•48; 95% CI 0•24, 0•99; linear trend P: 0•01).…”

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

“…Data mining integrates the theory and technology of many fields and has been widely used in various industries [2]. All kinds of massive data from the Internet pose new challenges to data mining technology [3]. e application of intelligent recommendation system to data mining technology is of innovative and practical significance, which can provide more targeted and intelligent information for people.…”

Mining ship deficiency correlations from historical port state control (PSC) inspection data

“…The FP-growth algorithm generates a candidate frequent itemset by applying the FP-tree data structure. The main weakness of the FP-growth algorithm is the large computation cost, and some frequent items may be missed due to sampling error [32][33][34][35].…”