Principles of Data Mining

With the requirement to find methods to analysis the rapidly increasing data, as a branch of computer science, data mining technique has been widely apply in different domain .One of the most enticing application areas of data mining in these emerging technologies is in finance. To complete the financial tasks, data mining technique is used refer to Stock market forecasting, Bankruptcy Prediction, Credit Risk Estimation, Corporate Performance Prediction and Management Fraud. This paper focuses on new data mining applications in finance. The fast development of Compute technology (such as Cloud Computing) and database together gives data mining a new challenge and chance in data mining of finance. How to apply the new computer and database technique to the data mining, and integrate the previous framework and the new technology together to build a more efficiency and safer framework is a long way to go in the future . Meanwhile, after accumulate enough empirical regularity and experiences, deep field-specific theories will be set up. This means, in the future, it will have more standardized and accurate model and method to apply in different domain of finance by generating more empirical regularities and combining them with domain knowledge.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

Indonesian EITI (Extractive Industries Transparency Initiative) is an organization under Ministry of Economic Coordination which used to increase the transparency of extractive industry in Indonesia. EITI Indonesia manage a lot of data about mining, one of the managed data is data Mining Business License in Indonesia. The data has many records that require large storage allocation and difficult process data that is used by the EITI for decision making. . This data Mining Business License will be used for the processing of data mining that aims to help look for interesting patterns to determine a learning and two itemsets (attributes) that exist. Application Data Mining with Emerging Patterns Supervised methods will be used as a solution for managing data such large, so it is easy to produce a decision in the form of an interesting pattern information to determine the transparency of mining license in Indonesia. System development methods using CRISP-DM. The design of data mining applications using the programming language Java, NetBeans and MySQL database tools used to build a technology Supervised Emerging Patterns in multi-attribute decision making.

Abstract- Utilization data mining in banking marketing strategy is very effective. Prospective customer seg-mentation is one of the processes carried out in the banking marketing strategy. To support the results of the success rate of telemarketing personnel to market the product in its role of banking services that the process requires a prospective customer data, then data mining support is very important in the classification of the prospective customers of the bank so that it can predict the degree of success in product marketing such services. Based on mapping studies of support data mining on prospective customers to come is no classification algorithms are often used for the classification of a borrower among others Neural Network, Naive Bayes, Dececion Tree, K-NN and Logistic Regreesion, of this algorithm can result from the evaluation process by using Cross Validation, confusion matrix, ROC Curve and T-Test to determine the classification of data mining algo-rithms are the most accurate in predicting success in product mar-keting telemarketing services from the bank to do trials in the Neural Network algorithm was more accurate with an accuracy of 89.71% the AUC value of 0872, this may be a comparison of data mining classi-fication Seeing AUC values of the five methods, then five groups of classification algorithms including both because of its AUC value between 0.80-1.00. Keywords : Comparison of data mining, decision tree, naive Bayes, neural network, k-nn, logistic regression

It is seen that distance education environments are more frequently used due to the progress of internet today. The data acquired from these environments are commonly irregular journal records and text-based communication messages. Digital mining applications are used in order to convert these irregular data into regular, and cleaning out the information stacks. The purpose of this research is to reveal the usage of the types of digital mining application, which are being applied within the distance education environments. For this purpose, a situation-based study was carried out which was related to the subjects of data mining, web mining and text mining, being used in the distance education environments. As a result of the research, a relationship was determined between the types of digital mining. Key words: Text mining, data mining, web mining.

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

In the era of big data, nursing staff have begun to use data mining technology to extract valuable information from massive nursing data to guide the development of clinical nursing, nursing management, nursing education and nursing research. This paper reviews the common methods of data mining, combs the application and research progress of data mining technology in nursing field, and puts forward the future development trend of data mining in nursing field, which can provide reference for nursing academia. Key words: Nursing; Data mining technology; Application

Replication is a widely used method for achieving high availability in database systems. The data replication approach can be used for solutions such as sharing data among remote offices, sharing data among dispersed users, make server data more accessible, distribute solution updates, back up data, provide internet or intranet replication. The main aim of this research is to provide a better technique for solving the drawbacks that currently exist in the literary works of data replica environment. Here, we intend to propose data replication system based on data mining techniques. That is in proposed method FDDRA (data mining based dynamic replication algorithm), we will use a data mining with combination of replication algorithm and data grid policy for data replication in the data mining environment through monitoring all data process. The data replication will be done by identifying the frequently used data patterns in the large database of a node. This will be done by Apriorialgorithm. The system availability and replica will be measured and identifying the location in which the replicated data will be stored. The result shows the efficiency of our algorithm in terms latency time and shortest path of nodes.

The choice of data mining algorithms has non-trivial nature. How to help users efficiently select an appropriate data mining algorithms draws a lot of researchers' attention nowadays in which the amount of data grows exponentially. This paper proposed a data mining web service recommendation engine which integrated collaborative filtering and association rule algorithms. It allows users share the experience on choosing data mining algorithms each other and it also can recommend appropriate data mining web services to users timely and effectively.

As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc. Keywords: Cloud computing, IOT, AI, face recognition, big data, data mining.

Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the discovered knowledge manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase in the objective function such as profit. In this paper, we present novel algorithms that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing an objective function: the expected net profit. These algorithms can discover cost-effective actions to transform customers from undesirable classes to desirable ones. The approach we take integrates data mining and decision making tightly by formulating the decision making problems directly on top of the data mining results in a postprocessing step. To improve the effectiveness of the approach, we also present an ensemble of decision trees which is shown to be more robust when the training data changes. Empirical tests are conducted on both a realistic insurance application domain and UCI benchmark data

The present work illustrates a Data Mining application in the meteorological domain. In particular, this work illustrates the creation of some fog classifying local indices, based on the post-processing of meteorological variables. A dataset containing a total amount of 17396 records, collected in Trapani Milo station and a poor quote of 142 fog events was obtained, such rare event required some specific approaches of the Data Mining techniques in order to overcome the class imbalance problem: a Cost Sensitive Classifier (matched with Bayes Network). The obtained results were evaluated by means of adequate performance metrics able to highlight the classifying ability of an index with respect to the fog events and the no-fog events separately (confusion matrix, ROC, AUC, …). The obtained models were tested over 4349 records; four models overcame the AUC threshold of 0.8 and, for one of them, the ROC curve showed a good result: 88% of fog events correctly predicted.

Data privacy becomes one of the most important aspects of Data Mining models designing. So far, most of studies on Data Mining algorithms where focused on efficiency and usability. One of a factor of achieved improvements is using a data warehouse as a source of analyzed data. In our work, we focus on the association rule mining within the parallel spatial data warehouse with data privacy preserving. According to the data model of the data warehouse, we use VPSI algorithm which operates on vertically partitioned data. The data protection mechanism uses a secure intersection of sets. The algorithm is based on the parallel version of Apriori algorithm, but instead of mining data on a transaction level, it operates on aggregated data. In our paper we analyze both aspects, efficiency and data privacy level of the algorithm.

A lot of important information were hidden in mass LIS data.Data mining technology can find useful data from multiple dimensions, providing information support for clinical diagnosis and treatment. This paper described the newly gained features of the laboratory medicine in the era of big data and analyzed the potential application of data mining in laboratory medicine.(Chin J Lab Med, 2015, 38: 888-890) Key words: Data mining; Medical laboratory science; Carcinoembryonic antigen; Cluster analysis

Normalizing data in the Adverse Event Reporting System (AERS), an FDA database, would improve the mining capacity of AERS for drug safety signal detection. In this study, we aim to normalize AERS and build a publicly available normalized Adverse drug events (ADE) data source.he drug information in AERS is normalized to RxNorm, a standard terminology source for medication. Drug class information is then obtained from the National Drug File - Reference Terminology (NDF-RT). Adverse drug events (ADE) are aggregated through mapping with the PT (Preferred Term) and SOC (System Organ Class) codes of MedDRA. Our study yields an aggregated knowledge-enhanced AERS data mining set (AERS-DM). The AERS-DM could provide more perspectives to mine AERS database for drug safety signal detection and could be used by research community in the data mining field.