An Incremental Density-Based Clustering Technique for Large Datasets

The very fast technological progress provokes the creation of a big volume of the astronomical data, which is fed in the different forms. There are various directions in science that use high dimensional data to be analyzed. In this paper we presented the few aspects of the "knowledge discovery in databases" (KDD) process in astronomy, analyzed and reviewed some data mining approaches. We presented the examples of astronomical instruments, data types, astronomical sources of Big Data, processing algorithms, which are used for the knowledge discovery in databases process in astronomy. The paper deals with applying the CoLiTec (Collection Light Technology) software for the online processing of the different types of astronomical data using the knowledge discovery in databases tasks. This is achieved by using of the developed OnLine Data Analysis System (OLDAS), which helps with solving of the knowledge discovery in databases tasks, like selection, transformation, data mining, interpretation, and evaluation.

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.

El presente trabajo pretende principalmente acercar a los investigadores del campo de las drogodependencias una metodología de análisis de datos orientada al descubrimiento de conocimiento en bases de datos (KDD). El KDD es un proceso que consta de una serie de fases, la más característica de las cuales se denomina Data Mining (DM), en la que se aplican diferentes técnicas de modelado para detectar patrones y relaciones en los datos. Se analizan los factores comunes y diferenciadores de las técnicas DM más ampliamente utilizadas, desde una visión principalmente metodológica, y ejemplificando su uso con datos provenientes del consumo de alcohol en adolescentes y su posible relación con variables de personalidad (N=7030). Aunque la precisión global obtenida (% de predicciones correctas) es muy similar en los tres modelos analizados, las redes neuronales generan el modelo más preciso (64.1%), seguidas de los árboles de decisión (62.3%) y Naive Bayes (59.9%).

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.

At institutions of higher education, data is generated daily. This massive amount of information is stored in different repositories, and it is increasingly difficult to locate specific data on which decisions can be made because universities are unaware of processes that allow for the extraction of valuable and reliable information. In this paper, we present a methodology that includes the Knowledge Discovery in Databases (KDD) coupled with the HEFESTO version 2.0 methodology for the construction of Data Warehouses and the use of Data Mining (DM) techniques. By implementing the proposed methodology, problems stemming from a lack of information relating to student placement and admissions in the UNAE and New Student Orientation (NSO) departments at the Polytechnic School of Chimborazo (ESPOCH) may be resolved. To accomplish this established goal, a Data Warehouse (DW) was implemented based on the requirements of the UNAE to find reliable information through cleaning and data integration techniques while respecting the Extraction, Transformation, and Load (ETL) process. In addition, several methods of DM were analyzed, culminating in the discovery of the pertinent information to ascertain the classification of students by areas of study, gender analysis, as well as to know the projection of the number of students who will commence university careers offered by ESPOCH in upcoming years.

The vast amounts of digital information stored in databases and other repositories represent a challenge for finding useful knowledge. Traditionalmethods for turning data into knowledge based on manual analysis reach their limits in this context, and for this reason, computer-based methods are needed. Knowledge Discovery in Databases (KDD) is the semi-automatic, nontrivial process of identifying valid, novel, potentially useful, and understandable knowledge (in the form of patterns) in data (Fayyad, Piatetsky-Shapiro, Smyth & Uthurusamy, 1996). KDD is an iterative and interactive process with several steps: understanding the problem domain, data preprocessing, pattern discovery, and pattern evaluation and usage. For discovering patterns, Data Mining (DM) techniques are applied.

The aim of this study is to highlight the importance of the unsupervised learning in Data mining and CRM fields. Data mining commonly known by its acronym KDD: knowledge discovery in data base, it refers to all methods and algorithms used for data exploration or prediction in large data bases volumes, Data mining is very important in various fields such as science, business and other areas deal with a large data set. CRM: Customer Relationship Management is an integrated information system that is used to plan, schedule and control the pre-sales and post-sales activities in an organization, both CRM and data mining techniques helps organizations maximize the value of every customer interaction and drive superior corporate performance. Clustering is one of the favoured used methods in data mining: The objective of this study is to implement the clustering algorithm K-Medoids via a shell script applied on a set of Analytical CRM data stored in Teradata environment.

A "Descoberta de Conhecimento em Bases de Dados" (Knowledge Discovery in Databases, KDD) é um processo composto de várias etapas, iniciando com a coleta de dados para o problema em pauta e finalizando com a interpretação e avaliação dos resultados obtidos. O presente trabalho objetiva mostrar a influência da análise exploratória dos dados no desempenho das técnicas de Mineração de Dados (Data Mining) quanto à classificação de novos padrões por meio da sua aplicação a um problema médico, além de comparar o desempenho delas entre si, visando obter a técnica com o maior percentual de acertos. Pelos resultados obtidos, pode-se concluir que a referida análise, se conduzida de forma adequada, pode trazer importantes melhorias nos desempenhos de quase todas as técnicas abordadas, tornando-se, assim, uma importante ferramenta para a otimização dos resultados finais. Para o problema em estudo, a técnica que envolve um modelo de Programação Linear e uma outra que envolve Redes Neurais foram as técnicas que apresentaram os menores percentuais de erros para os conjuntos de testes, apresentando capacidades de generalização satisfatórias.

Data mining, or Knowledge Discovery in Databases (KDD), is of little benefit to commercial enterprises unless it can be carried out efficiently on realistic volumes of data. Operational factors also dictate that KDD should be performed within the context of standard DBMS. Fortunately, relational DBMS have a declarative query interface (SQL) that has allowed designers of parallel hardware to exploit data parallelism efficiently. Thus, an effective approach to the problem of efficient KDD consists of arranging that KDD tasks execute on a parallel SQL server. In this paper we devise generic KDD primitives, map these to SQL and present some results of running these primitives on a commercially-available parallel SQL server.

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.

Recent advances in information technology have made it possible to solve increasingly complex problems, and also to collect and store huge amounts of information. These vast quantities of data further have to be transformed into relevant value-added and “decision-quality” knowledge. It is against this background that the KDD (Knowledge Discovery in Databases), a multidisciplinary field using computer learning, artificial intelligence, statistics, database technology, expert systems, and data visualization, appeared in the early 90's. In order to assess these technologies in the medical field, we have tested some of these techniques on a large database at our disposal, named DiabCare stemming from the WHO – DiabCare program for the application of the Saint-Vincent Declaration. It contains evaluation data on the health care of patients with diabetes, and in particular, its complications. So far, data analysis has been done using classical statistical methods, and we now intend to make use of such data-mining tools as Associations Rules and Decision and Classification Trees for further exploration of this database. The results presented here show that data mining techniques can be used successfully to extract knowledge from medical databases. The results obtained using Association Rules and especially Decision Trees are very promising.

Reports on conceptual development in the areas of database mining and knowledge discovery in databases (KDD). Our efforts have also led to a prototype implementation, called MOTC, for exploring hypothesis spaces in large and complex data sets. Our KDD conceptual development rests on two main principles. First, we use the crosstab representation for working with qualitative data. This is by now standard practice in OLAP (online analytical processing) applications, and we reaffirm it with additional reasons. Second, and innovatively, we use prediction analysis as a measure of goodness for hypotheses. Prediction analysis is an established statistical technique for analysis of associations among qualitative variables. It generalizes and subsumes a large number of other such measures of association, depending upon the specific assumptions the user is willing to make. As such, it provides a very useful framework for exploring the hypothesis space in a KDD context. This paper illustrates these points with an extensive discussion of MOTC.

Parameters influencing the microbial oxidation activity in the industrial bioleaching heap at Escondida mine, Chile

Like other data-rich disciplines such as physics, biology, geology, and oceanography, astronomy is facing a data avalanche due to advances in telescope and detector technology, the exponential increase in computing capabilities, improvements in data-collection methods, and successful applications of theoretical simulations. As the era approaches in which data covers the full range of wavelengths from radio to gamma-rays, the expected data volumes will add up to terabytes, soon to be followed by petabytes. Proper management and processing of massive data sets requires efficient federation of database technologies. However, mining knowledge from huge data volumes is the ultimate goal and development of data-mining techniques is therefore critical. Knowledge discovery in databases (KDD) is the process of extracting useful knowledge from data. Data mining, the application of specific algorithms to discover rare or previously unknown types of object or phenomenon, is a particular step in the process.1 KDD is inherently interactive and iterative, as shown in Figure 1. Common KDD functions are classification, cluster analysis, and regression. In classification one develops a description or model for each class of data labeled with discrete integers (as opposed to cluster analysis, which is sometimes called ‘unsupervised classification’). Classification is used for the organization of future test data, better understanding of each data class, and predictions of certain properties and behaviors. It is based on spectra or images and, for example, may be used to describe galaxies by morphology. Cluster (or clustering) analysis is a multivariate procedure based on placing objects into more or less homogeneous groups such that the relationship between groups is revealed. It lacks an underlying body of statistical theory and is heuristic in nature, requiring decisions to be made by individual users (which can strongly affect results). Cluster analysis is used to classify groups or objectsmore objectively than subjectively and can help astronomers find unusual objects within a flood of data. ExamFigure 1. Knowledge discovery in databases.

Knowledge discovery in databases, or data mining, is an important objective in the development of data- and knowledge-base systems. An attribute-oriented rough set method is developed for knowledge discovery in databases. The method integrates learning from example techniques with rough set theory. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of the database learning processes. Then the rough set techniques are applied to the generalized relation to derive different knowledge rules. Moreover, the approach can find all the maximal generalized rules in the data. Based on these principles, a prototype database learning system, DBROUGH, has been constructed. Our study shows that attribute-oriented induction combined with rough set techniques provide an efficient and effective mechanism for knowledge discovery in database systems.