Wavelet methods in data mining

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.

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 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.

Intelligent Data Analysis (IDA) approach proves its ability to deal with small and huge data sets; this ability can be done by generating and developing new methodologies. While; Knowledge Discovery in Database (KDD) aiming to automatic interpretation of large data sets. As a result, we can conclude a strong relationship between two concepts. This study discusses five challenges related to data analytical to making right decision. These challenges include: Missing values; Data scarcity, Data dimensionality reduction, Black box; and Mathematical model. First: One of the important trends in KDD will be the growing importance of data processing. But this point faces problems similar to those of data mining (High dimensional data, missing values imputation and data integration). As explained before, one of the open still problems in estimation missing values methods are how to select the optimal number of nearest neighbors of those values. Second: Data scarcity (insufficient size of dataset) problem is one of the challenges in KDD. Insufficient size of data is very often responsible for the poor performances in learning. Third: The goal of dimension reduction methods is using the correlation structure among the predicator variables to reduction the three main dimensions (features, samples and value of features). But, how we can combination among these three dimensions without lose any important inform is still as one of the challenges in KDD system. Fourth: As we know data mining algorithm are “black box character” for many reasons explained in chapter two. But how we can convert it to system will behaviors and conclusion that can be explained and analyzed in a comprehensible manner (i.e., white box) is remained one of the main challenges of KDD system.

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.

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 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.

SMART software for decision makers KDD experience

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 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.

Based on an overview on recent contributions with respect to data mining applications terms like “data mining”, “data warehousing” and “knowledge discovery in databases” are related to the well-known discussion where “data analysis” and “decision support” are combined with “expert knowledge research”. Next, commercial data mining tools are compared with the help of positioning and segmentation procedures to get a feeling for the support provided by software tailored for performing tasks concerning knowledge discovery in databases. Finally, an own modification of an association rule algorithm is used to handle buying histories in the area of consumer behavior interpretation.

 Abstract—Nowadays, the method of data mining, widely known as KDD (Knowledge-discovery in databases), is researched actively due to increasing amount of data. Decision tree, which is human-readable and understandable, is widely used in the field of rule extraction, data mining, and pattern recognition. Although decision tree is easy for quickly understanding the data, it has many faults in algorithm itself. In this paper, to compensate for these faults, we combined decision table (rough set theory) and ANN (Artificial Neural Network). With this combined algorithm, we could easily obtain more precise rules from iris data. Our experiment was in this order: 1) Find weights by using back propagation (bp) algorithm. (ANN). 2) Use dimensionality reduction method to delete huge amount of irrelevant data. (Rough set theory and decision table). 3) Make decision tree with pruned weights and rules. With ANN algorithm and decision table (dimensionality reduction method), we could easily notice the most influential (high entropy) character of iris data. With this algorithm, we could extract more precise and pruned rule from iris data sets.

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.

Telecommunications are one of the most dynamics and strategic areas in the world. In a complex and competitive scenario with pressure for better results with fewer resources, there is a constant need in the organizations for searching new and improved forms of administration with better understanding of the processes and customers. Techniques such as knowledge discovery in databases (KDD) and data mining (DM) appear as alternatives for enabling transform raw and complex data in models that may allow better decision making. This paper presents an application of multivariate regression analysis for modeling and explaining sales in the segment of corporate telecommunications, through acting indicators. A series of comparative analyses were accomplished with intend to identify the most appropriate or impacting indicators to perform the best decisions when selling corporate services. It is also presented a systemization of KDD activities which integrates the methodologies such as CRISPDM and SEMMA in an interactive design framework. Variable selection methods and statistical validation of the model are presented. Results are presented from real databases of large Brazilian telecommunications company.