Evolving learners’ behavior in data mining

This research study analyzes the different data mining techniques used for the detection of credit card frauds. Additionally, different data mining techniques are properly described in this study. The steps of data mining are clearly explained in this study, which are helpful for recognizing the fraudulent activities. This study is discussing that Bayesian network, and decision tree are the effective techniques of data mining. The issues of data mining are also discussed in this study, which is creating performance issues, and user interaction issues in the financial institutions. Therefore, this study is providing the importance of RF algorithm in the determination of the credit card fraud. Data cleaning, and data visualization, therefore, the machine learning process is allowed to be developed with the support of the data mining process of credit cards. Clustering is the main process that is takes place in this data mining process. Additionally, the sequential pattern is being highlighted by this process, which helps to improve the fraud detection process of credit cards.

Intelligent computing system based on pattern recognition and data mining algorithms

Nowadays, the world is still in the environment of economic depression. In order to promote economic recovery, improve Relations of production and production efficiency, stimulate consumption expansion and upgrading, and accelerate industrial transformation and upgrading, problems such as industrial upgrading need to be solved urgently. Solving the above problems requires more useful tools, and artificial intelligence is one of them. Machine learning is the key to distinguishing artificial intelligence from ordinary program code. Unlike people learning knowledge, machine learning has its own unique language algorithms and behavioral logic. Machine learning, as a technology active in the field of artificial intelligence in recent years, specializes in studying how computers learn, simulate and realize part of human learning behavior, so as to provide data mining and behavior prediction for humans, to obtain new knowledge or skills, or to strengthen the original basic ability of machines. In this study, a variety of common coding algorithms and learning strategies in machine learning are discussed, supervised learning algorithms are selected as examples in the learning strategies, models are further selected and evaluated for a variety of algorithms, and parameters are adjusted and performance is analyzed. As for the theoretical analysis in the research, the paper makes a tentative application in the three fields of housing price, physical store sales and digital recognition, explores and selects the corresponding application method in the appropriate scenario, and expands the application field of machine learning.

Feature selection plays a central role in data analysis and is also a crucial step in machine learning, data mining and pattern recognition. Feature selection algorithm focuses mainly on the design of a criterion function and the selection of a search strategy. In this paper, a novel feature selection approach (NFSA) based on quantum genetic algorithm (QGA) and a good evaluation criterion is proposed to select the optimal feature subset from a large number of features extracted from radar emitter signals (RESs). The criterion function is given firstly. Then, detailed algorithm of QGA is described and its performances are analyzed. Finally, the best feature subset is selected from the original feature set (OFS) composed of 16 features of RESs. Experimental results show that the proposed approach reduces greatly the dimensions of OFS and heightens accurate recognition rate of RESs, which indicates that NFSA is feasible and effective.

Granular maximum decision entropy-based monotonic uncertainty measure for attribute reduction

The field of Smart Healthcare has undergone a revolution due to the development of machine learning technologies. Artificial intelligence (AI) has a subfield called machine learning that allows computers to learn from data and develop over time without explicit programming. Healthcare workers can now discover trends, diagnose diseases, and make decisions based on more precise and timely data thanks to machine learning's capacity to comprehend and analyze enormous amounts of data. Finding patterns and links in massive datasets is known as data mining. Data mining is used in Smart Healthcare to locate patterns in patient data that can be utilized to comprehend a patient's health status better, diagnose specific disorders, and forecast the most effective course of therapy. Data mining can create novel cures and treatments customized to a person's genetic profile. Data mining in Smart Healthcare is now considerably more effective and efficient, thanks to machine learning. Using complex algorithms, machine learning models can analyze vast amounts of data markedly faster and more correctly than conventional methods. Healthcare experts may not immediately notice hidden patterns and connections in data that machine learning algorithms can find. As a result, machine learning can assist healthcare practitioners in making decisions that are better informed, improving patient outcomes.

Because of huge impacts of "OMICS" technologies in life sciences, many researchers aim to implement such high throughput approach to address cellular and/or molecular functions in response to any influential intervention in genomics, proteomics, or metabolomics levels. However, in many cases, use of such technologies often encounters some cybernetic difficulties in terms of knowledge extraction from a bunch of data using related softwares. In fact, there is little guidance upon data mining for novices. The main goal of this article is to provide a brief review on different steps of microarray data handling and mining for novices and at last to introduce different PC and/or web-based softwares that can be used in preprocessing and/or data mining of microarray data. To pursue such aim, recently published papers and microarray softwares were reviewed. It was found that defining the true place of the genes in cell networks is the main phase in our understanding of programming and functioning of living cells. This can be obtained with global/selected gene expression profiling. Studying the regulation patterns of genes in groups, using clustering and classification methods helps us understand different pathways in the cell, their functions, regulations and the way one component in the system affects the other one. These networks can act as starting points for data mining and hypothesis generation, helping us reverse engineer.

Feature selection is a dimensionality reduction method known as a main step in data mining and machine learning. The aim of feature selection is to remove redundant and unrelated features. In recent years several feature selection methods based on graph theory and social networking techniques have been proposed .In this study, a feature selection approach based on multi objective PSO algorithm and social network techniques is presented. In the proposed method, Fisher score, node centrality and edge centrality are used to construct the fitness function in order to present a multi objective particle swarm optimization (PSO) approach. The proposed method run over a variety of datasets and the results are compared with the well-known filter-based feature selection methods. The results show that the proposed method is effective and the performance of the proposed method better than other methods in some cases.

Data clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as ‘neutrosophic c-means’ (NCM) was proposed in order to alleviate the limitations of the popular fuzzy c-means (FCM) clustering algorithm by introducing a new objective function which contains two types of rejection. The ambiguity rejection which concerned patterns lying near the cluster boundaries, and the distance rejection was dealing with patterns that are far away from the clusters. In this paper, we extend the idea of NCM for nonlinear-shaped data clustering by incorporating the kernel function into NCM. The new clustering algorithm is called Kernel Neutrosophic c-Means (KNCM), and has been evaluated through extensive experiments. Nonlinear-shaped toy datasets, real datasets and images were used in the experiments for demonstrating the efficiency of the proposed method. A comparison between Kernel FCM (KFCM) and KNCM was also accomplished in order to visualize the performance of both methods. According to the obtained results, the proposed KNCM produced better results than KFCM.

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry.

Preprocessing is one of the steps in Data Mining, which involves Noise removal, Identification of outlier, Normalization, Data transformation, Handling missing values, etc. Missing value is a common problem in large datasets. Most frequently used method to handle missing values by statistical is discarding the instances with missing values. Sometime deletion of instances with missing values cause loss of essential information, which affects the performance of statistical and machine learning algorithms. This paper focuses on handling missing values using unsupervised learning techniques. Rough K-Means based missing value imputation was proposed and compared with K-Means, Fuzzy C-Means based imputation methods. The experimental analysis is carried out on two data sets Lung Cancer and Cleveland Heart data sets. The proposed method achieves the best accuracy for some of the datasets.

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms.

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.

This paper presents detailed technique and steps of data mining in sluice safety monitoring according to the monitoring data’s features. The tasks and objectives of data mining are defined and converted into the knowledge which can be realized by certain algorithm. The methods of data preprocessing is also discussed to eliminate or reduce redundant, noise, incomplete and inconsistent data. The examples of data preprocessing and data mining show how to apply the data mining technology in the sluice safety monitoring filed, which results indicate that the method presented in this paper is effective and can be used in other projects.

This paper mainly discusses the theory of data mining, discusses the application of decision tree technology in the construction project cost data management system. This paper mainly studies the following contents: firstly, the development of informatization and data mining of construction project cost data management is introduced, and then the management problems of project cost data management are introduced; secondly, the concept and steps of data mining are introduced, and then the common algorithms of data mining are briefly introduced, This paper describes the specific application of data mining in related industries.