A Survey on Evolutionary Instance Selection and Generation

Joaquín Derrac; Salvador García; Francisco Herrera

doi:10.4018/jamc.2010102604

A Survey on Evolutionary Instance Selection and Generation

Modeling, Analysis, and Applications in Metaheuristic Computing ◽

10.4018/978-1-4666-0270-0.ch014 ◽

2012 ◽

pp. 233-266 ◽

Cited By ~ 1

Author(s):

Joaquín Derrac ◽

Salvador García ◽

Francisco Herrera

Keyword(s):

Data Mining ◽

Evolutionary Algorithms ◽

Nearest Neighbor ◽

Instance Selection ◽

Data Sets ◽

Generation Process ◽

Data Mining Algorithms ◽

Instance Generation ◽

Nearest Neighbor Rule ◽

Mining Algorithms

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

Cloud for Distributed Data Analysis Based on the Actor Model

Scientific Programming ◽

10.1155/2016/1050293 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Ivan Kholod ◽

Ilya Petukhov ◽

Andrey Shorov

Keyword(s):

Data Mining ◽

Data Analysis ◽

Data Sets ◽

Distributed Data ◽

Actor Model ◽

Confidential Information ◽

Data Mining Algorithms ◽

Functional Blocks ◽

Mining Algorithms ◽

Distributed Data Analysis

This paper describes the construction of a Cloud for Distributed Data Analysis (CDDA) based on the actor model. The design uses an approach to map the data mining algorithms on decomposed functional blocks, which are assigned to actors. Using actors allows users to move the computation closely towards the stored data. The process does not require loading data sets into the cloud and allows users to analyze confidential information locally. The results of experiments show that the efficiency of the proposed approach outperforms established solutions.

Introduction to Fuzzy Data Mining Methods

Handbook of Research on Fuzzy Information Processing in Databases ◽

10.4018/978-1-59904-853-6.ch003 ◽

2011 ◽

pp. 55-95 ◽

Cited By ~ 12

Author(s):

Balazs Feil ◽

Janos Abonyi

Keyword(s):

Data Mining ◽

Model Performance ◽

Fuzzy Rule ◽

Classification Problem ◽

Data Sets ◽

Rule Based ◽

Data Mining Algorithms ◽

Rule Bases ◽

Mining Algorithms ◽

Extraction Model

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

PSO Optimized Nearest Neighbor Algorithm

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3574.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 1508-1513

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Heuristic Algorithms ◽

Optimization Methods ◽

Classification Model ◽

Natural Phenomenon ◽

Wide Applicability ◽

Data Mining Algorithms ◽

Wide Range ◽

Mining Algorithms

Data mining can be considered to be an important aspects of information industry. Data mining has found a wide applicability in almost every field which deals with data. Out of the various techniques employed for data mining, Classification is a very commonly used tool for knowledge discovery. Various alternatives methods are available which can be used to create a classification model, out of which the most common and apprehensible one is KNN. In spite of KNN having a number of shortcomings and limitations in it, these can be overcome by with the help of alterations which can be made to the basic KNN algorithm. Due to its wide applicability, kNN has been the focus of extensive research and as a result, many alternatives have been performed with wide range of success in performance improvement. A major hardship being faced by the data mining applications is the large number of dimensions which render most of the data mining algorithms inefficient. The problem can be solved to some extent by using dimensionality reduction methods like PCA. Further improvements in the efficiency of the classification based mining algorithms can be achieved by using optimization methods. Meta-heuristic algorithms inspired by natural phenomenon like particle swarm optimization can be used very effectively for the purpose.

A Comparison Study of Data Mining Algorithms for blood Cancer Prediction

passer ◽

10.24271/psr.29 ◽

2019 ◽

Vol 3 (1) ◽

pp. 174-179

Author(s):

Noor Bahjat ◽

Snwr Jamak

Keyword(s):

Data Mining ◽

Machine Learning Algorithms ◽

Common Disease ◽

Data Sets ◽

Blood Cancer ◽

Cancer Data ◽

Data Mining Algorithms ◽

Detection And Diagnosis ◽

Mining Algorithms ◽

Early Detection And Diagnosis

Cancer is a common disease that threats the life of one of every three people. This dangerous disease urgently requires early detection and diagnosis. The recent progress in data mining methods, such as classification, has proven the need for machine learning algorithms to apply to large datasets. This paper mainly aims to utilise data mining techniques to classify cancer data sets into blood cancer and non-blood cancer based on pre-defined information and post-defined information obtained after blood tests and CT scan tests. This research conducted using the WEKA data mining tool with 10-fold cross-validation to evaluate and compare different classification algorithms, extract meaningful information from the dataset and accurately identify the most suitable and predictive model. This paper depicted that the most suitable classifier with the best ability to predict the cancerous dataset is Multilayer perceptron with an accuracy of 99.3967%.

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i1.pp75-87 ◽

2020 ◽

Vol 18 (1) ◽

pp. 75

Author(s):

M. Jupri ◽

Riyanarto Sarno

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Fuzzy Ahp ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Mining Algorithms ◽

Using Data ◽

Time Required ◽

Mining Algorithms

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

Trajectory Pattern Mining: Methods and Applications

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.490-491.1361 ◽

2014 ◽

Vol 490-491 ◽

pp. 1361-1367

Author(s):

Xin Huang ◽

Hui Juan Chen ◽

Mao Gong Zheng ◽

Ping Liu ◽

Jing Qian

Keyword(s):

Data Mining ◽

Social Media ◽

Pattern Mining ◽

Data Sets ◽

Trajectory Data ◽

Data Mining Algorithms ◽

Mining Methods ◽

Trajectory Pattern ◽

Mining Algorithms ◽

Trajectory Pattern Mining

With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.

The Research of High Efficient Data Mining Algorithms for Massive Data Sets

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3901 ◽

2014 ◽

Vol 556-562 ◽

pp. 3901-3904

Author(s):

Cui Xia Tao

Keyword(s):

Data Mining ◽

Data Sets ◽

Modern Information Technology ◽

Incremental Processing ◽

Data Mining Algorithms ◽

High Efficient ◽

Data Volume ◽

Efficient Data ◽

Valid Solution ◽

Mining Algorithms

Data mining means to extract information and knowledge that potentially useful while still unknown in advance, from a large quantity of implicit incomplete, random data. With the quick advancement of modern information technology, people are accumulating data volume on the increase sharply, often at the speed of TB. How to extract meaningful information from large amounts of data has become a big problem must be tackled. In view of the huge amounts of data mining, distributed parallel processing and incremental processing is valid solution.

Workload Optimization by Horizontal Aggregation in SQL for Data Mining Analysis

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit217263 ◽

2021 ◽

pp. 304-309

Author(s):

Prasanna M. Rathod ◽

Prof. Dr. Anjali B. Raut

Keyword(s):

Data Mining ◽

Relational Algebra ◽

Data Migration ◽

Data Sets ◽

Data Set ◽

Application Performance ◽

Data Mining Algorithms ◽

Mining Project ◽

Pivot Methods ◽

Mining Algorithms

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables, and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g., point-dimension, observation variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: ? CASE: Exploiting the programming CASE construct; ? SPJ: Based on standard relational algebra operators (SPJ queries); ? PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not. For query optimization the distance computation and nearest cluster in the k-means are based on SQL. Workload balancing is the assignment of work to processors in a way that maximizes application performance. The process of load balancing can be generalized into four basic steps: 1. Monitoring processor load and state; 2. Exchanging workload and state information between processors; 3. Decision making; 4. Data migration. The decision phase is triggered when the load imbalance is detected to calculate optimal data redistribution. In the fourth and last phase, data migrates from overloaded processors to under-loaded ones.

Data mining fool’s gold

Journal of Information Technology ◽

10.1177/0268396220915600 ◽

2020 ◽

Vol 35 (3) ◽

pp. 182-194

Author(s):

Gary Smith

Keyword(s):

Data Mining ◽

Scientific Method ◽

Large Data ◽

Learning Systems ◽

Large Data Sets ◽

Data Sets ◽

Computer Algorithms ◽

Data Mining Algorithms ◽

Rigorous Testing ◽

Mining Algorithms

The scientific method is based on the rigorous testing of falsifiable conjectures. Data mining, in contrast, puts data before theory by searching for statistical patterns without being constrained by prespecified hypotheses. Artificial intelligence and machine learning systems, for example, often rely on data-mining algorithms to construct models with little or no human guidance. However, a plethora of patterns are inevitable in large data sets, and computer algorithms have no effective way of assessing whether the patterns they unearth are truly useful or meaningless coincidences. While data mining sometimes discovers useful relationships, the data deluge has caused the number of possible patterns that can be discovered relative to the number that are genuinely useful to grow exponentially—which makes it increasingly likely that what data mining unearths is likely to be fool’s gold.