A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Comparative Analysis of Various Data Stream Mining Procedures and Various Dimension Reduction Techniques

In recent years data mining is contributing to be the great research area, as we know data mining is the process of extracting needful information from the given set of data which will be further used for various purposes, it could be for commercial use or for scientific use .while fetching the information (mined data) proper methodologies with good approximations have to be used .In our survey we have provided the study about various data stream clustering techniques and various dimension reduction techniques with their characteristics to improve the quality of clustering, we have also provided our approach(our proposal) for clustering the streamed data using suitable procedures ,In our approach for stream data mining a dimension reduction technique have been used then after the Fuzzy C-means algorithm have been applied on it to improve the quality of clustering. Keywords: Data Stream, Dimension Reduction, Clusterin

Algorithmic Techniques for Processing Data Streams

We give a survey at some algorithmic techniques for processing data streams. After covering the basic methods of sampling and sketching, we present more evolved procedures that resort on those basic ones. In particular, we examine algorithmic schemes for similarity mining, the concept of group testing, and techniques for clustering and summarizing data streams

A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

Data-Mining a Large Digital Sky Survey: From the Challenges to the Scientific Results

The analysis and an efficient scientific exploration of the Digital Palomar Observatory Sky Survey (DPOSS) represents a major technical challenge. The input data set consists of 3 Terabytes of pixel information, and contains a few billion sources. We describe some of the specific scientific problems posed by the data, including searches for distant quasars and clusters of galaxies, and the data-mining techniques we are exploring in addressing them. Machine-assisted discovery methods may become essential for the analysis of such multi-Terabyte data sets. New and future approaches involve unsupervised classification and clustering analysis in the Giga-object data space, including various Bayesian techniques. In addition to the searches for known types of objects in this data base, these techniques may also offer the possibility of discovering previously unknown, rare types of astronomical objects.Comment: Invited paper, to appear in Applications of Digital Image Processing XX, ed. A. Tescher, Proc. S.P.I.E. vol. 3164, in press; 10 pages, a self-contained TeX file, and 3 separate postscript figure

A Review on Various Methods of Intrusion Detection System

Detection of Intrusion is an essential expertise business segment as well as a dynamic area of study and expansion caused by its requirement. Modern day intrusion detection systems still have these limitations of time sensitivity. The main requirement is to develop a system which is able of handling large volume of network data to detect attacks more accurately and proactively. Research conducted by on the KDDCUP99 dataset resulted in a various set of attributes for each of the four major attack types. Without reducing the number of features, detecting attack patterns within the data is more difficult for rule generation, forecasting, or classification. The goal of this research is to present a new method that Compare results of appropriately categorized and inaccurately categorized as proportions and the features chosen. Data mining is used to clean, classify and examine large amount of network data. Since a large volume of network traffic that requires processing, we use data mining techniques. Different Data Mining techniques such as clustering, classification and association rules are proving to be useful for analyzing network traffic. This paper presents the survey on data mining techniques applied on intrusion detection systems for the effective identification of both known and unknown patterns of attacks, thereby helping the users to develop secure information systems. Keywords: IDS, Data Mining, Machine Learning, Clustering, Classification DOI: 10.7176/CEIS/11-1-02 Publication date: January 31st 2020

The Influence of Data Mining in Increasing Benefits of Libraries in Jordanian Governmental Universities

This current study aimed at examining the impact of data mining on increasing benefits of university library in Jordanian governmental university. Researcher adopted techniques of data mining which included (Association, Classification, Clustering, Prediction, Sequential Patterns and Decision Trees). Through employing the quantitative approach and utilizing a questionnaire as a study tool, (412) responded to an online survey which primary data later on was screened and processed using SPSS v. 27th. Results of study accepted the main hypothesis as there appeared an influence of data mining in better organization flow and accumulation of library data and better develop library\u27s services for users. Among data mining techniques, it appeared that (Sequential pattern, decision trees and Prediction techniques) were the most influential techniques on library services followed by librarians in developing library services, this was noticed through the high correlation which connected them to the dependent variable, and the remaining variables also appeared to be positive in influence with a medium correlation. Study recommended to better data mining application by responsible parties within Jordanian universities as there appeared an acceptable level of application; however, the application isn\u27t used to its maximum capacity

Importance of Similarity Measure in Gene Expression Data-A Survey

The usage of data mining techniques in research fields of computational biology include gene finding, genome assembly , prediction of gene expression etc, are very promising because the large amount of data is involved in these research fields. These techniques aims that to disclose the unknown knowledge and relationships. Different data sources are available one such as DNA Micro Array is the technology which enables the researchers to investigate and address issues which are non traceable. DNA Micro Array experiments generates thousands of gene expression measurements and provide a simple way for collecting huge amounts of data in short time. Micro array data analysis allows identifying the most relevant genes for a target disease and group of genes with similar patterns under different experimental conditions.Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. The goal of clustering in micro array technology is to group genes or experiments into clusters according to a similarity measure. In this paper we introduce the concept of micro Array technology, clustering on gene expression data and survey on similarity measure. Finally we conclude this paper promising that similarity measure plays an important role on gene expression data while using one of the data mining techniques is clustering