A glance at DNA microarray technology and applications.

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.

An evaluation and choice of learning algorithms is a current research area in data mining, artificial intelligence and pattern recognition, etc. Supervised learning is one of the tasks most frequently used in data mining. There are several learning algorithms available in machine learning field and new algorithms are being added in machine learning literature. There is a need for selecting the best suitable learning algorithm for a given data. With the information explosion of different learning algorithms and the changing data scenarios, there is a need of smart learning system. The paper shows one approach where past experiences learned are used to suggest the best suitable learner using 3 meta-features namely simple, statistical and information theoretic features. The system tests 38 UCI benchmark datasets from various domains using nine classifiers from various categories. It is observed that for 29 datasets, i.e., 76 % of datasets, both the predicted and actual accuracies directly match. The proposed approach is found to be correct for algorithm selection of these datasets. New proposed equation of finding classifier accuracy based on meta-features is determined and validated. The study compares various supervised learning algorithms by performing tenfold cross-validation paired t test. The work helps in a critical step in data mining for selecting the suitable data mining algorithm.

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.

The intelligent prediction of fault is an important and difficult modern technique in our world, and the Technique can assure the safe running of key equipment and improve the working condition of it. The Technique is mainly based on large and complex electromechanical equipment, research and disclosure the new methods and technique on intelligent prediction of fault based on data mining. Through the characteristic of fault data of electromechanical system, the acquisition methods of fault prediction based on data mining will be found out; the flow of fault prediction based on data mining will be set up; the fault prediction system and relative engineering application software will be made based on data mining. The test shows: the key technique for intelligent prediction of fault in safe running of complex electromechanical equipment can improve the efficiency of fault prediction for large electromechanical equipment.

This chapter deals with the fundamentals of swarm intelligence, focusing mainly on particle swarm optimization and ant colony optimization for solving multi-objective problems in data mining and knowledge discovery in databases. Besides providing a general overview of the field, we identify some of the multiple criteria which are tightly/loosely coupled in various steps of knowledge discovery and data mining, aiming to direct the attention of researchers towards them in the near future.

Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an 'organic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.

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.

A survey on single and multi omics data mining methods in cancer data classification.

Extracting useful Web content is a major step in data mining. The Web content extraction process is very important for many technologies or uses as a preprocessing of many systems such as crawlers and indexers. Additionally, the extracted content is needed by the end users especially for blind and visually impaired users. It aims to extract useful and meaningful data from Webpages that are surrounded with various clutters such as advertisements and navigation menus. Many extraction algorithms are designed for English Language and perform less efficient and less accurate in Arabic language. In this paper, a bi-languages mining algorithm for extracting Web contents called BiLEx is presented. It extracts useful Web content from Arabic and English Webpages in the approximately same level of efficiency and accuracy. An experiment is made for 600 Webpages which are chosen randomly from 30 different Websites to test the proposed algorithm performance and efficiency. Results prove that BiLEx algorithm gives high precision, recall, and F1-measure for both Arabic and English Webpages.

An incremental approach to attribute reduction from dynamic incomplete decision systems in rough set theory

An improved hybrid chameleon swarm algorithm for feature selection in medical diagnosis

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 quality has diverse dimensions, from which accuracy is the most important one. Data cleaning is one of the preprocessing steps in data mining which consists of detecting errors and repairing them. Noise is a common type of error, that occur in database. This paper proposes an automated method based on the k-means clustering for noise detection. At first, each attribute (Aj) is temporarily removed from data and the k-means clustering is applied to other attributes. Thereafter, the k-nearest neighbors is used in each cluster. After that a value is predicted for Aj in each record by the nearest neighbors. The proposed method detects noisy attributes using predicted values. Our method is able to identify several noises in a record. In addition, this method can detect noise in fields with different data types, too. Experiments show that this method can averagely detect 92% of the noises existing in the data. The proposed method is compared with a noise detection method using association rules. The results indicate that the proposed method have improved noise detection averagely by 13%.

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.