Data Mining – Concepts and Techniques

International Conference on Data Science and Applications (DSA 2020) will act as a major
forum for the presentation of innovative ideas, approaches, developments, and research
projects in the areas of Data Science and Applications. It will also serve to facilitate the
exchange of information between researchers and industry professionals to discuss the latest
issues and advancement in the area of Data Science & Applications

Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.

The Internet may be free, but service provider’s indispensable to access services are not, to the extent that while the complexity and burden of the sites increases, it is becoming more and more expensive to surf the net. Blocking access under the guise of protecting us from offensive or sexually explicit content, to pages, chat rooms, newsgroups and other Web options is not anymore an excuse, but a lie. Government surveillance in the Internet, uncontrolled practices of data harvesting and restriction of free speech, open discussion of issues and even political activism has spread in the last 20 years to include countries that are considered democracies such as the U.S. Internet has become a battleground of power and therefore become increasingly militarized.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data

7th International Conference on Data Mining & Knowledge Management (DaKM 2022) provides
a forum for researchers who address this issue and to present their work in a peer-reviewed forum.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in the
following areas, but are not limited to these topics only.

We describe a nonparametric algorithm based on the empirical distribution function which can be used for classification and prediction. The EDFs of at least ordinally-scaled variables are used to classify or predict the value of a target variable. The algorithm uses a function of the quantiles of the EDFs of independent variables to determine the nearest value of a dependent variable. The algorithm may be extended to binary-valued variables by assuming that there is a superior class and an inferior class, and that they may be labeled ‘1’ and ‘0’, respectively. The algorithm is robust with respect to missing data.

The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning
techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database.
The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the
experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.

Stress will exist once operating or paying attention to somebody shouts at you. It is caused through conflicts, creating selections, or otherwise straining skills, or it will exist owing to various social demands on time. it's noted that emotional states tends to not be long lasting, whether or not they are unpleasant emotions like worry, or pleasant ones, like joy. Nonetheless infrequently such states might persist for long periods of your time, or they will reach too high levels. Once this happens, the result typically is labeled "stress", and it's psychological as well as physiological terms. Stress and its manifestations, such as anxiety, depression, and burnout, have always have been a common problem among people in different professions and occupations. In the last few decades, alarm has already been provoked by the proliferation of books, research reports, popular articles and the growing number of organized workshops, aiming to teach people how to cope up with it. The purpose of the study is to find out the level of academic stress among higher secondary students. The present study consists of students studying in higher secondary schools situated in Tamil Nadu, India. The sample was selected by using simple random sampling technique. The present study reveals that the higher secondary students are having moderate level of academic stress and irrespective of sub samples of the higher secondary students are having moderate level of academic stress. The male student's academic stress is higher than female students. The urban student's tutorial stress is higher than rural students. The government college student's tutorial stress is a smaller amount than school students. The science subject student's tutorial stress is over arts student. The scholars whose parents are literates, the educational stress is over their counterparts.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.

Call for Papers 7 th International Conference on Data Mining & Knowledge Management (DaKM 2022) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.

In the current era, the amount of data generated from various device sources and business transactions is rising exponentially, and the current machine learning techniques are not feasible for handling the massive volume of data. Two commonly adopted schemes exist to solve such issues scaling up the data mining algorithms and data reduction. Scaling the data mining algorithms is not the best way, but data reduction is feasible. There are two approaches to reducing datasets selecting an optimal subset of features from the initial dataset or eliminating those that contribute less information. Overweight and obesity are increasing worldwide, and forecasting future overweight or obesity could help intervention. Our primary objective is to find the optimal subset of features to diagnose obesity. This article proposes adapting a bagging algorithm based on filter-based feature selection to improve the prediction accuracy of obesity with a minimal number of feature subsets. We utilized several machine learning algorithms for classifying the obesity classes and several filter feature selection methods to maximize the classifier accuracy. Based on the results of experiments, Pairwise Consistency and Pairwise Correlation techniques are shown to be promising tools for feature selection in respect of the quality of obtained feature subset and computation efficiency. Analyzing the results obtained from the original and modified datasets has improved the classification accuracy and established a relationship between obesity/overweight and common risk factors such as weight, age, and physical activity patterns.

Artificial intelligence techniques are still revealing their pros; however, several fields have benefited from these techniques. In this study we applied the Decision Tree (DT-CART) method derived from artificial intelligence techniques to the prediction of the creditworthy of bank customers, for this we used historical data of bank customers. However we have adopted the flowing process, for this purpose we started with a data preprocessing in which we clean the data and we deleted all rows with outliers or missing values, then we fixed the variable to be explained (dependent or Target) and we also thought to eliminate all explanatory (independent) variables that are not significant using univariate analysis as well as the correlation matrix, then we applied our CART decision tree method using the SPSS tool. After completing our process of building our model (DT-CART), we started the process of evaluating and testing the performance of our model, by which we found that the accuracy and precision of our model is 71%, so we calculated the error ratios, and we found that the error rate equal to 29%, this allowed us to conclude that our model at a fairly good level in terms of precision, predictability and very precisely in predicting the solvency of our banking customers.

Landslides are the major natural disaster in hill country of Sri Lanka, which create terrible economical and ecological damages. Therefore, the fast detection is important. Currently in Sri Lanka,predict landslides based on a map reading approach. But a map is limited to specific point in time, and do not take current conditions into account. Therefore, develop a model/tool which has ability to efficiently deal with current situation is important. Hence within this study, prediction models were developed using Decision Tree and Neural Network data mining techniques,based on the data of Badulla and NuwaraEliya districts. Selected Decision Tree model for Badulla district has 96.2963% accuracy level and Nuwara Eliya district has 100% accuracy level. Though Decision tree models were outperformed, Neural Network models also have above 90% accuracy. Therefore, it can be concluded that both data mining techniques are suitableto developlandslide prediction models for Sri Lanka.

Computer-aided technologies (CAx) mean the use of computer technology to aid in the design, analysis and manufacture various products. International Journal of Computer-Aided technologies (IJCAx)  is an open access, peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Aided technologies such as CAD, CAM, CAIM,CAR, CARD, CASE etc. The journal focuses on all technical and practical aspects of Computer Aided technologies. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced CAx tools and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in all Computer Aided technologies

Today's era is an era of modernization and globalization. Everything is happening at a very fast rate whether it is politics, societal reforms, commercialization, transportation, or educational innovations. In every few second, technology grows either in the form of arrival of the new devices/gadgets with millions of apps and these latest technological objects may be in the form of hardware/software devices. We are the educationists, teachers, students and stakeholders of present Indian educational system. These gadgets/devices are partly being used by us or most of them are still unaware of these innovative technologies due to the mass media or economical factor. So, there is a need to improvise ourselves towards utilizing the future gadgets in order to explore the educational uses, barriers and preparatory-needs of these available devices for educational purposes. This paper aims to study the opinion of the teacher-educators about the usage of future gadgets in higher education. It will also contribute towards establishing the list of latest technological devices, and how it can enhances the process of teaching-learning system.

In this paper is analyzed the design of a software platform concerning a case study of process engineering involving the simultaneous adoption of data digitation, Data Mining-DM-processing, and Augmented Reality-AR-. Specifically is discussed the platform design able to upgrade the Knowledge Base-KB-enabling production process optimizations in working sites. The KB is gained by following 'Frascati' research guidelines addressing the possible ways to achieve the Knowledge Gain-KG-. The technologies such as AR and data entry mobile app are tailored in order to apply innovative data mining algorithms. In the first part of the paper is commented the preliminary project specifications, besides, in the second part, are shown the use cases, the unified modeling language-UML-models, and the mobile app mockups enabling KG. The proposed work discusses preliminary results of an industry project.

There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

In this paper we show apply text mining techniques, Correspondence Analysis and Fuzzy C-Means Clustering in order to identify associations among countries and titles of documents available at a profile in Academia.edu. All analysis was carried out by R software and several R scripts are presented.

Information security is a vital aspect of any organization. Most of the organizations relay and trust on the intrusion Detection System (IDS) which play important role in detecting intrusions in data network environment. The design of IDS varies with implementation of different IDS techniques involved. The design of IDS techniques keep changes as the trend of data network innovative attack methods gets updated day by day. Hence there is no single perfect solution is found for detecting the intrusions in the data network. In general IDS systems are complex and it is an ongoing process. There are dissimilar types of intrusion detection systems exist and pass through a common problem of rendering high volume of alerts and immense number of false positives. The false positive alert alters the space and time complexities of the IDS modules and gradually slows down the detection rate and performance of the system. This is the main motive behind the research of this paper. The objective of this research paper is to explore and suggest different techniques which help design in building the optimal intrusion Detection system of low cost and high performing computational capability and adaptability to various network environments for the results of false alert reduction, a high intrusion detection rate, risk management both detection and control the intrusions, finally to identify the real attacks from other false alarms and events of the system. This paper navigates through different associated studies of the last decade with providing a citation for further research in this domain. Various unresolved issues have also been covered in this manuscript.

Today in this real world, where we deal with vast amounts of data, we are in a situation to extract the best among them. Data
mining is a new and imminent trend to discover the information and knowledge from the existing data. The gained knowledge can be used
for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration. It uses
machine learning, statistical and visualization techniques to discover the present knowledge in the form which is easily understandable by the
users. The data mining tools are used to predict the future trends and behaviours of various business organi sations and aid them to make good decisions for their growth and development. They search the databases for hidden patterns, and to make predictions beyond the mind-set of the
experts. This paper explains about various open source data mining tools through which efficient predictive analysis can be done.

Understanding the evolution and emergence of technology domains remains a challenge, particularly so for potentially breakthrough technologies. Though it is well recognized that emergence of new fields is complex and uncertain, to make decisions amidst such uncertainty, one needs to mobilise various sources of intelligence to identify known-knowns and known-unknowns to be able to choose appropriate strategies and policies. This competitive technical intelligence cannot rely on simple trend analyses, breakthrough technologies have little past to inform such trends, and the directions of evolution are difficult. Neither do qualitative tools, embracing the complexities, provide all the solutions, since transparent and repeatable techniques need to be employed to create best practices and evaluate the intelligence that comes from such exercises. In this paper, we present a hybrid roadmapping technique that draws on a number of approaches and integrates them into an approach that can be applied to breakthrough technology fields and integrates a multi-level approach (individual activities, industry evolutions and broader global changes). We describe this approach in deeper detail with a case study on dye-sensitized solar cells. Our contribution to this special issue is to showcase the technique as part of a family approaches which are emerging around the world to inform strategy and policy.

Peranan listrik sangat penting bagi setiap lapisan masyarakat bahkan listrik juga sangat dibutuhkan sebagai sarana produksi dan untuk kehidupan sehari-hari, begitu pentingnya peranan listrik tentu saja berdampak pada permintaan listrik yang semakin besar tapi hal ini kiranya tidak linier dengan persediaan listrik yang belum mampu memenuhi permintaan listrik yang begitu besar tersebut. Untuk mengatasi hal ini perlu adanya campur tangan pemerintah dan masyarakat dalam menggunakan listrik dengan bijak sehingga kebutuhan listrik tidak menjadi lebih besar dari persediaan listrik. Oleh karena itu setiap rumah tangga haruslah paham penggunaan listrik yang efektif. Penerapan metode naïve bayes diharapkan mampu untuk memprediksi besarnya penggunaan listrik tiap rumah tangga agar lebih mudah mengatur penggunaan listrik. dari 60 data penggunaan listrik rumah tangga yang diuji dengan metode naïve bayes, maka diperoleh hasil persentase 78,3333% untuk keakuratan prediksi, di mana dari 60 data penggunaan listrik rumah tangga yang diuji terdapat 47 data penggunaan listrik rumah tangga yang berhasil diklasifikasikan dengan benar.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Web is a collection of interrelated files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, E-government, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper, we have studied the basic concepts of web mining, classification, processes and issues. In addition to this, this paper also analyzed the web mining research challenges.

Data mining and knowledge discovery of database is magnetizing wide array of non-trivial research arena, making easy to industrial decision support systems and continues to expand even beyond imagination in one such promising field like Artificial Intelligence and facing the real world challenges. Association rules  forms an important paradigm in the field of data mining for various databases like transactional database, time-series database, spatial, object-oriented databases etc. The burgeoning amount of data in multiple heterogeneous sources coalesces with the impediment in building and preserving central vital repositories compels the need for effectual distributive mining techniques. The majority of the previous studies rely on an Apriori-like candidate set generation-and-test approach. For these applications, these forms of aged techniques are found to be quite expensive, sluggish and highly subjective in case there exists long length patterns.

13th International conference on Database Management Systems (DMS 2022) will provide
an excellent international forum for sharing knowledge and results in theory, methodology and
applications of Database Management Systems. The goal of this conference is to bring together
researchers and practitioners from academia and industry to focus on understanding Modern
developments in this field and establishing new collaborations in these areas.
Authors are solicited to contribute to this conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Database management systems.

Abstract— Context: Data Mining (DM) method has been evolving year by year and as of today there is also the enhancement of DM technique that can be run several times faster than the traditional one, called Distributed Data Mining (DDM). It is not a new field in data processing actually, but in the recent years many researchers have been paying more attention on this area. Problems: The number of publication regarding DDM in high reputation journals and conferences has increased significantly. It makes difficult for researchers to gain a comprehensive view of DDM that require further research. Solution: We conducted a systematic literature review to map the previous research in DDM field. Our objective is to provide the motivation for new research by identifying the gap in DDM field as well as the hot area itself. Result: Our analysis came up with some conclusions by answering 7 research questions proposed in this literature review. In addition, the taxonomy of DDM research area is presented in this paper. Finally, this systematic literature review provides the statistic of development of DDM since 2000 to 2015, in which this will help the future researchers to have a comprehensive overview of current situation of DDM.

Google classifies software raters as software end-users and established that raters’ experience is a good criterion for judgement of software quality on online software repositories (OSR). Knowing the number of end-users helps in planning and management of repositories, software development and software usage for countries and OSR managers. Scaffidi’s estimation of number of end-users for America for year 2012 was done based on data collected from work places in America. This research uses non-linear data transformation technique for obedient of linearity assumption and double sampling statistical tool to estimate the number of end-users on a sourceforge OSR using OkikiSoft data mining software for data collection. It was established that there existed 3,889,712 end-users on the sourceforge OSR. Similarly, rating of the software categories on sourceforge OSR was done based on the estimated number of end-users. It was further recommended that OSR researchers and managers could, respectively, use the estimated results for subsequent inference and repository management.

The integration of XML data sources which have different schemas/DTD can originate structural and vocabular heterogeneity. In this context, it is difficult to write satisfiable queries. As a solution, many Information Systems focus on building approximate evaluation techniques for exact queries. As a project, we build flexible and preference XML query languages and associated evaluation algorithms. In this paper, we propose the Flexible Preference Tree Pattern Query (FPTPQ), a new TPQ that allows multiple items/names (resp. paths) for the same node, in order to integrate (resp. to locate) all the different instances of the database nodes. The FPTPQ enable to have preference nodes and ordering operators among label items and paths. We also provide a holistic algorithm that evaluates the FPTPQ and capitalises the preferences to determine the best available solutions. Illustrations and experimentations are realized to show the effectiveness of our solutions.

Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and
heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems
operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing
with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the
availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints
for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly
scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for
information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios
and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large
dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently
constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale
model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and
analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different
exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction
and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough
theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based
prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we
provide a performance evaluation and a empirical user study employing real datasets.

This paper will discuss the general relationship between data mining tools and data warehousing system, especially on how the data needs to be prepared in the data warehouse before being used by a data mining tool, and how the result of data mining can be put back to the data warehouse to allow more broad access or reuse. The paper will also use a specific examples to demonstrate how the quality of data stored in a data warehousing will impact the result of data mining

This contribution presents a brief history of softwares for identifying of
typical daily load pro¯les developed in the Brazilian electric power sector since the
eighties. In order to tell this history we present the features of three softwares that
have been used by the Brazilian electric distribution utilities, all of then with data
mining techniques for clustering.