Deployment of Partitioning Around Medoids Clustering Algorithm on a Set of Objects Derived from Analytical CRM Data

Data mining (knowledge discovery in data base) is comparatively new interdisciplinary field developed by joint efforts of mathematicians, statisticians, computer scientists and engineers. There are twelve important ingredients of this field along with their applications in real world problems. In this chapter, we have reviewed application of wavelet methods to data mining, particularly denoising, dimension reduction, similarity search, feature extraction and prediction. Meteorological data of Saudi Arabia and Stock market data of India are considered for illustration.

Information extraction (IE) and knowledge discovery in databases (KDD) are both useful approaches for discovering information in textual corpora, but they have some deficiencies. Information extraction can identify relevant sub-sequences of text, but is usually unaware of emerging, previously unknown knowledge and regularities in a text and thus cannot form new facts or new hypotheses. Complementary to information extraction, emerging data mining methods and techniques promise to overcome the deficiencies of information extraction. This research work combines the benefits of both approaches by integrating data mining and information extraction methods. The aim is to provide a new high-quality information extraction methodology and, at the same time, to improve the performance of the underlying extraction system. Consequently, the new methodology should shorten the life cycle of information extraction engineering because information predicted in early extraction phases can be used in further extraction steps, and the extraction rules developed require fewer arduous test-and-debug iterations. Effectiveness and applicability are validated by processing online documents from the areas of eHealth and eRecruitment.

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

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.

Data mining, also known as knowledge discovery in databases, is a statistical analysis technique used to find hidden patterns and identify untapped value in large datasets. Clustering is a principal data discovery technique in data mining that segregates a dataset into subsets or clusters so that data values in the same cluster have some common characteristics or attributes. A number of clustering techniques have been proposed in the past by many researchers that can identify arbitrary shaped cluster; where a cluster is defined as a dense region separated by the low-density regions and among them DBSCAN is a prime density-based clustering algorithm. DBSCAN is capable of discovering clusters of any arbitrary shape and size in databases which even include noise and outliers. Many researchers have attempted to overcome certain deficiencies in the original DBSCAN like identifying patterns within datasets of varied densities and its high computational complexity; hence a number of augmented forms of DBSCAN algorithm are available. We present an incremental density-based clustering technique which is based on the fundamental DBSCAN clustering algorithm to enhance its computational complexity. Our proposed algorithm can be used in different knowledge domains like image processing, classification of patterns in GIS maps, x-ray crystallography and information security.

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.

Knowledge discovery in databases and data mining aim at semiautomatic tools for the analysis of large data sets. We consider some methods used in data mining, concentrating on levelwise search for all frequently occurring patterns. We show how this technique can be used in various applications. We also discuss possibilities for compiling data mining queries into algorithms, and look at the use of sampling in data mining. We conclude by listing several open research problems in data mining and knowledge discovery.

Preterm births in the United States increased from 11.0% to 11.4% between 1996 and 1997; they continue to be a complex healthcare problem in the United States. The objective of this research was to compare traditional statistical methods with emerging new methods called data mining or knowledge discovery in databases in identifying accurate predictors of preterm births. An ethnically diverse sample (N = 19,970) of pregnant women provided data (1,622 variables) for new methods of analysis. Preterm birth predictors were evaluated using traditional statistical and newer data mining analyses. Seven demographic variables (maternal age and binary coding for county of residence, education, marital status, payer source, race, and religion) yielded a .72 area under the curve using Receiving Operating Characteristic curves to test predictive accuracy. The addition of hundreds of other variables added only a .03 to the area under the curve. Similar results across data mining methods suggest that results are data-driven and not method-dependent, and that demographic variables offer a small set of parsimonious variables with reasonable accuracy in predicting preterm birth outcomes in a racially diverse population.

The grant program for farmers is one of the government programs provided by the Cianjur Regency Agriculture Office. To accept the program, farmers submit proposals which will be reviewed by the proposal reviewer analyst. In making a decision to accept a grant proposal, it is necessary to make a quick and accurate decision. With quite a lot of proposal data that has been submitted, it can be used data patterns that have been stored with data mining methods to predict proposals that are accepted with proposals that are rejected. The algorithm used is K-Nearest Neighbor where the determination of acceptance of new proposal data is based on the closest distance to the acceptance of the previous proposal. There are six predictor variables used, namely assets, capital, business scale, length of business, cost plan and completeness of documents. The stages of data mining use the stages in Knowledge Discovery in Databases (KDD). Calculations use the weight of the assessment for each proposal criteria that have been determined. The results of the evaluation of the calculation of the kNN algorithm obtained a real accuracy of 76% and an error rate of 24%. From the calculation results, the kNN algorithm can be used as a model to determine recipients of agricultural grant programs. Program pemberian dana hibah untuk para petani merupakan salah satu program pemerintah yang diberikan oleh Dinas Pertanian Kabupaten Cianjur. Untuk menerima program tersebut petani mengajukan proposal yang akan diperiksa oleh analis pemeriksa proposal. Dalam mengambil keputusan penerimaan proposal hibah perlu adanya keputusan yang cepat dan akurat. Dengan cukup banyaknya data proposal yang telah diajukan , maka dapat digunakan pola data yang telah tersimpan dengan metode data mining untuk memprediksi proposal yang diterima dengan proposal yang ditolak. Algoritma yang digunakan yaitu K-Nearest Neighbor di mana penentuan penerimaan data proposal baru berdasarkan pada jarak terdekat dengan penerimaan proposal sebelumnya. Terdapat enam variabel prediktor yang digunakan yaitu aset, modal, skala usaha, lama usaha, rencana biaya dan kelengkapan dokumen. Tahapan data mining menggunakan tahapan dalam knowledge Discovery in Databases (KDD) . Perhitungan menggunakan bobot penilaian untuk setiap kriteria proposal yang sudah ditentukan. Hasil evaluasi perhitungan algoritma kNN diperoleh akurasi sebenar 76% dan tingkat error sebesar 24%. Dari hasil perhitungan, algoritma kNN ini dapat digunakan sebagai model untuk menentukan penerima program hibah pertanian.

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

Data mining or knowledge discovery in database (KDD) refers to the non-trivial process of discovering interesting, implicit, and previously unknown knowledge from databases.Such a task implies to be able to perform analyses both on high-dimensional input data and large dataset.The most popular models used in KDD are the symbolic models.Unfortunately, these models suffer of very serious limitations.Rule generation is a highly time-consuming process that generates a huge number of rules, including a large ratio of redundant rules.Hence, this prohibits any kind of rule computation and selection as soon as data are numerous and they are represented by very high-dimensional description space.This latter situation is very often encountered with documentary data.To cope with these problems, preliminary KDD trials using numerical models have been made.An algorithm for knowledge extraction from self-organizing network has been proposed in [8].This approach is based on a supervised generalized relevance learning vector quantization (GRLVQ) which is used for extracting decision trees.The different paths of the generated trees are then used for denoting rules.Nevertheless, the main defect of this method is to necessitate training data.On our own side, we have proposed a hybrid classification method for mapping an explicative structure issued from a symbolic classification into an unsupervised numerical self-organizing map (SOM) [15].SOM map and Galois lattice are generated on the same data.The cosine projection is then used for associating lattice concepts to the SOM classes.Concepts properties act as explanation for the SOM classes.Furthermore, lattice pruning combined with migration of the associated SOM classes towards the top of the pruned lattice is used to generate explanation of increasing scope on the SOM map.Association rules can also be produced in such a way.Although it establishes interesting links between numerical and symbolic worlds this approach necessitates the time-consuming computation of a whole Galois lattice.In a parallel way, in order to enhance both the quality and the granularity of the data analysis and to reduce the noise which is inevitably generated in an overall classification approach, we have introduced the multi-viewpoint analysis and multilevel clustering approach based on a significant extension of the SOM model, named MultiSOM [19][25].The viewpoint building principle consists in separating the description of the data into several sub-descriptions corresponding different property subsets or even different data subsets.In MultiSOM each viewpoint is represented by a single SOM map.www.intechopen.com

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.

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 and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and Data Mining, and Knowledge Discovery in Database (KDD) process are presented in the paper. Data mining theory, Data mining tasks, Data Mining technology and Data Mining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.

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