Data Mining and Knowledge Discovery from Store Separation Trajectories

Computational scientists must understand results from experimental, observational and computational simulation generated data to gain insights and perform knowledge discovery. As systems approach the petascale range, problems that were unimaginable a few years ago are within reach. With the increasing volume and complexity of data produced by ultra-scale simulations and high-throughput experiments, understanding the science is largely hampered by the lack of comprehensive I/O, storage, acceleration of data manipulation, analysis, and mining tools. Scientists require techniques, tools and infrastructure to facilitate better understanding of their data, in particular the ability to effectively perform complex data analysis, statistical analysis and knowledge discovery. The goal of this work is to enable more effective analysis of scientific datasets through the integration of enhancements in the I/O stack, from active storage support at the file system layer to MPI-IO and high-level I/O library layers. We propose to provide software components to accelerate data analytics, mining, I/O, and knowledge discovery for large-scale scientific applications, thereby increasing productivity of both scientists and the systems. Our approaches include 1) design the interfaces in high-level I/O libraries, such as parallel netCDF, for applications to activate data mining operations at the lower I/O layers; 2) Enhance MPI-IO runtime systems to incorporate the functionality developed as a part of the runtime system design; 3) Develop parallel data mining programs as part of runtime library for server-side file system in PVFS file system; and 4) Prototype an active storage cluster, which will utilize multicore CPUs, GPUs, and FPGAs to carry out the data mining workload.

Medical datasets hold huge number of records about the patients, the doctors and the diseases. The extraction of useful information which will provide knowledge in decision making process for the diagnosis and treatment of the diseases are becoming increasingly determinant. Knowledge Discovery and data mining make use of Artificial Intelligence (AI) algorithms which are applied to discover hidden patterns and relations in complex datasets using intelligent agents. The existing data mining algorithms and techniques are designed to solve the individual problems, such as classification or clustering. Up till now, no unifying theory is developed.Among the different algorithms in data mining for prediction, classification, interpretation and visualization, ‘k-means clustering’, ‘Decision Trees (C4.5)’, ‘Neural Network (NNs)’ and ‘Data Visualization (2D or 3D scattered graphs)’ algorithms are frequently utilized in data mining tools. The choice of the algorithm depends on the intended use of extracted knowledge.In this paper, the mentioned algorithms are unified into a tool, called Unified Medical Data Miner (UMDM) that will enable prediction, classification, interpretation and visualization on a diabetes dataset.KeywordsMedicineClusteringClassification & PredictionVisualizationAgent Data miningUnified Medical Data Miner

The smart grid is an electrical grid that has a duplex communication. This communication is between the utility and the consumer. Digital system, automation system, computers and control are the various systems of Smart Grid. It finds applications in a wide variety of systems. Some of its applications have been designed to reduce the risk of power system blackout. Dynamic vulnerability assessment is done to identify, quantify, and prioritize the vulnerabilities in a system. This paper presents a novel approach for classifying the data into one of the two classes called vulnerable or non-vulnerable by carrying out Dynamic Vulnerability Assessment (DVA) based on some data mining techniques such as Multichannel Singular Spectrum Analysis (MSSA), and Principal Component Analysis (PCA), and a machine learning tool such as Support Vector Machine Classifier (SVM-C) with learning algorithms that can analyze data. The developed methodology is tested in the IEEE 57 bus, where the cause of vulnerability is transient instability. The results show that data mining tools can effectively analyze the patterns of the electric signals, and SVM-C can use those patterns for analyzing the system data as vulnerable or non-vulnerable and determines System Vulnerability Status.

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

Banking industry is very competitive and the market itself has become very saturated in the recent years. Because of this, it is vital for every banking institution to use all available information in order to gain some sort of competitive advantage. In this paper we will demonstrate that it is possible to gain more insight about banking service clients by using data mining and analysis tools, more specifically by using K-Means clustering. Specifically, we will demonstrate that we can define a group of customers that are more prone to purchase housing loan.

Data Mining refers to a set of methods applicable to large and complex databases to eliminate the randomness and discover the hidden pattern. Datamining (DM), also known as knowledge discovery from databases (KDD), is the extraction of new knowledge from huge databases. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Data mining tools can forecast the future trends and activities to support the decision of people. The scope of datamining is associated with Uncovering trends and patterns are a great power for the businesses of all sectors and industries. Modern intrusion detection applications are confronted with a variety of issues. These applications must be reliable, extensible, manageable, and minimal in maintenance costs. Data mining-based intrusion detection systems (IDSs) have shown high accuracy, good generalisation to novel types of intrusion, and stable behaviour in a changing environment in recent years. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear. The main purpose of this work is to identify privacy and security concerns among cloud computing participants and consumers in a distributed environment. Techniques like Machine Learning, Natural Language Processing (NLP), and Data Mining are combined to automatically identify and uncover patterns from many sorts of materials. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics. The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). The surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds. The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. Grid-based clustering has the shortest processing time, which is typically determined by the size of the grid rather than the data. We compare the performance of three clustering algorithms: hierarchical clustering, density-based clustering, and K Means clustering. The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. We provide a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques.

The thriving medical applications of data mining in the fields of medicine and public health has led to the popularity of its use in knowledge discovery in databases (KDD). Data mining has revealed novel biomedical and healthcare acquaintances for clinical decision making that has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Disease diagnosis is one of the applications where data mining tools are establishing the successful results. Data mining intends to endow with a systematic survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research. Discussion is made to enable the disease diagnosis and the breakthrough of hidden healthcare patterns from related databases is offered. Also, the use of data mining to discover such relationships as those between health conditions and a disease is presented. It further discusses about the tools that can be used for the processing and classification of data. This paper summarizes various technical articles on medical diagnosis and prognosis. It has also been focused on current research being carried out using the data mining techniques to enhance the disease(s) forecasting process. This research paper provides future trends of current techniques of KDD, using data mining tools for healthcare. It also confers significant issues and challenges associated with data mining and healthcare in general. The research found a growing number of data mining applications, including analysis of health care centers for better health policy-making, detection of disease outbreaks and preventable hospital deaths. The root causes of all diseases get closer towards drugs i.e. the foremost risk factor of all hilarious diseases. Drug addiction using WEKA has been used that brings into light concerning majority of drug abusers started abusing drugs at age below 20yrs.It is to make aware the druggist about the various diseases that are caused with heavy or long term intake of drugs in their life. So, to make an expert system that will awake the youth about precarious use of drugs and also alert the affected person.

In this paper is presented an experimental study with Data Mining (DM) tools, such as, CMSR Data Miner, KnowledgeSTUDIO and RapidMiner. It is goal to discover at that problems (classification, regression, clustering, association, among others) may be used, including, to verify if can treat Big Data, these tools were applied to three databases: Breast Cancer, Iris and Wine (obtained from the Machine Learning repository from University of California). In the experiments with the CMSR Data Miner tool, the Decision Tree and SOM (Self Organizing Maps) Neural Network algorithms of Data Mining were used. After that, with the KnowledgeSTUDIO tool, Logistic Regression, Deep Learning and K-Means algorithms were used. And finally, with the RapidMiner tool, Decision Tree, Naive Bayes and Deep Learning algorithms of Data Mining were applied. Additionally, it is also with this research aim to decision making by organizational managers, helping them to choose tools from DM more appropriate to their daily work.

Most team sports are characterized by positions to which the players in a team are assigned. The goal of this classification is to attribute specific responsibilities during a game. Moreover, the same classification drives the buying and selling of players according to team managers and coaches strategies. The existing positions - often defined a long time ago - tend to reflect traditional points of view about the game and sometimes they are no longer well-suited to the new concepts arisen with the evolution of the way of playing. This paper focuses on basketball and aims at describing new roles of players during the game, by means of the analysis of players' performance statistics with data mining and machine learning tools. In detail, self-organizing maps and fuzzy clustering procedures are adopted in tandem to define groups of players with similar way of playing. The results show that, when considering the modern basketball players' statistics, classical positions are not able to fully represent their way of playing, and a new set of 5 roles emerges as a meaningful classification of players' characteristics.

Autism Spectrum Disorder (ASD) is a contemporary disease that has recently spread among toddlers and children. Many researchers have been interested to determine the features of the autism. However, this kind of studies is costly in term of the gathering information from several sources. In this paper, we introduced and applied a novel and early prediction techniques based on the using of data mining and machine learning tools. It is difficult to determine the features of any autism ages. In this paper, we used data mining predication techniques which play an integral role to predict the symptoms of autism for any age group. The data of this study is AQ-10 dataset which are involved for toddlers and children. The results present a superior performance for ASD classification. Random forest, Decision Tree, Support Vector Machine, and Naive Bayes the accuracy of 1.0 with the features selected by correlation technique.

Many prediction models have been developed using data mining tools in order to predict the performance of the students at the early stage. The academic performance of higher education students commonly was predicted based on their results in the end of the previous semester or during the semester like test score or mid-term exam. However, there is lack of models that emphasize the use of data related to student’s behaviour for predicting the academic performance. Therefore, the aim of this study is to investigate the use of self-efficacy behaviour data to predict the academic performance of students using principal component analysis (PCA) and k-means clustering (KMC). This study focuses on the first part of the prediction which is model development. In the model development phase, the number of variables were reduced from 20 into two by using PCA. The scores for the first two principal components were clustered by using KMC. The results show that the scores can be clustered into three main clusters to differentiate the student’s self-efficacy behaviour. Next research will investigate the underlying causes of the clusters in order to predict the risky students.

Clustering is a process of classifying data into different classes and has become an important tool in data mining. Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simplicity and high efficiency. However, the traditional K-means algorithm can only find spherical clusters, and is also susceptible to noise points and isolated points, which makes the clustering results affected. To solve these problems, this paper proposes an improved K-means algorithm based on kurtosis test. The improved algorithm can improve the adaptability of clustering algorithm to complex shape datasets while reducing the impact of outlier data on clustering results, so that the algorithm results can be more accurate. The method used in our study is known as kurtosis test and Monte Carlo method. We validate our theoretical results in experiments on a variety of datasets. The experimental results show that the proposed algorithm has larger external indicators of clustering performance metrics, which means that the accuracy of clustering results is significantly improved.

As an analysis tool either of on-line analytical processing (OLAP) or data mining has its own strong points and weaknesses. It is an effective way to enhance the power and flexibility of data mining in data warehouses and large databases by integrating data mining with OLAP to offset their weaknesses. In the paper, a proposal of integrating data mining and OLAP is put forward. The mechanism of the on-line analytical data mining in data warehouses is described. Aspects which are necessary for developing a successful on-line analytical data mining system is also discussed.

Knowledge discovery in databases (KDD) and data mining represent very important tools for processing and analysing data in large databases. The combination of powerful KDD and data mining techniques and sophisticated resources of object-oriented database systems brings even more considerable results. These emerging tools and techniques require a powerful data mining query language serving as an interface between applications and data mining tools. This motivates us to design an object-oriented data mining query language for mining different kinds of knowledge from object-oriented databases. We introduce an overview of this language, its syntax and semantics in this paper and some examples of practical use will be shown too.KeywordsData MiningAssociation RuleClassification RuleRule TypeSequential RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data mining or knowledge discovery in the database (KDD) is an excellent process to find out valuable information from a large collection of data. Data mining has successfully been used in different fields such as medical, marketing, banking, business, weather forecasting, etc. For the banking industry, data mining, its importance, and its techniques are vital because it helps to extract useful information from a large amount of historical data which enable to make useful decisions. Data mining is very useful for banking sector for better acquiring and targeting new customers and helps to analyze customers and their transaction behaviors. In the recent era, a new technology that has achieved considerable attention, especially among banks, is internet banking. Its large scope of applications, its advantages brings an immoderate change in a common human's life. Linear regression is one of the most commonly used and applied data mining techniques. Linear regression is really a very fast and simple regression algorithm and can give the best performance if the output variable of your data is a linear grouping of your inputs. In this paper, the linear regression is applied on internet banking adoption dataset in order to compute the weights or coefficients of linear expression and provides the predicted class value. The analysis here is done with the help of WEKA tool for data mining.