Application of data mining techniques using SAS software

Data mining has captured the hearts and minds of business analysts seeking a solution forexploring and modeling vastly larger, more complex and less well-behaved datasets. Exploratorydata analysis, typically consisting of activities like statistical visualization, hypothesis generation,and introductory model fitting is a vital first step in any successful data mining venture.Exploratory data analysis produces direct benefits for data miners in enhanced understanding ofdata, improved clarity and confidence of the modeling results, and avoidance of pitfalls early inthe process. By using data mining techniques to analyze the data that is accumulating and fillingvast data warehouses, organizations can harness more insight from their large data stores to driveproactive decision making. SAS data mining software can surface patterns and trends in yourdata that you may never have thought to look for. This paper will review the usefulness of SAS Tsoftware for exploratory data analysis, interactive regression modeling, and advancedmultidimensional data visualizatio

Comment: Classifier Technology and the Illusion of Progress

Comment on Classifier Technology and the Illusion of Progress [math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000024 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

DATA MINING AND THE PROCESS OF TAKING DECISIONS IN EBUSINESS

Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, in some ways, an extension of statistics, with a few artificial intelligence and machine learning twists thrown in. Like statistics, data mining is not a business solution, it is just a technology. For example, consider a catalog retailer who needs to decide who should receive information about a new product. The information operated on by the data mining process is contained in a historical database of previous interactions with customers and the features associated with the customers, such as age, zip code, their responses. The data mining software would use this historical information to build a model of customer behavior that could be used to predict which customers would be likely to respond to the new product. By using this information a marketing manager can select only the customers who are most likely to respond. The operational business software can then feed the results of the decision to the appropriate touch point systems (call centers, direct mail, web servers, email systems, etc.) so that the right customers receive the right offers.data mining, business decisions, data analysis, cluster analysis, decision strategy

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

Association Rule Mining

Tato bakalářská práce se zabývá dolováním asociačních pravidel. První část se věnuje vysvětlení technologie dolování dat a teorie, kterou je dobré znát pro seznámení se s asociační analýzou. Další část se věnuje samotné asociační analýze a podrobně vysvětluje principy algoritmu Apriori. Poslední část práce popisuje implementaci a testování algoritmu Apriori v programovacím jazyce Java.This bachelor's thesis is concerned with the association rule mining. The first part is devoted to the explanation of data mining technology and theory, which are necessary pre-steps for getting acquainted with association analysis. The next part focuses on the association analysis itself and explains the principals of algorithm Apriori in detail. The last part of the thesis describes the implementation and testing of algorithm Apriori in the Java programming language.

Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth

--- Perkembangan pasar modern yang semakin hari semakin pesat dapat dilihat dari pusat perbelanjaan seperti supermarket, minimarket, grosir, dan lain sebagainya yang dibangun untuk kebutuhan melayani konsumen. Dan pemanfaatan data transaksi yang banyak dapat memberikan pengetahuan yang menarik dalam membuat kebijakan dan strategi penempatan rak barang. Maraknya perbelanjaan modern dan pesaing bisnis seperti itu tidak lepas dari peralihan pola pikir konsumen yang tadinya mencari harga yang murah, kini sudah memperhatikan aspek keamanan, kebersihan, Kenyamanan, keramahan dalam pelayanan serta kelengkapan jenis barang dan penempatan rak barang. Oleh karena itu dalam penelitian ini, penulis mengangkat permasalahan tentang Analisa Pola Belanja Swalayan Daily Mart Untuk Menentukan Tata Letak Barang Menggunakan Algoritma FP-Growth, dalam pelayanan yang sering terjadi di swalayan Daily Mart, dan untuk mewujudkan hal itu penulis menerapkan metodologi KDD (Knowledge Discovery in Database). Salah satu teknik Data Mining dalam penelitian ini adalah Association Rule dalam Java Weka untuk mencari pengetahuan pola dari pembelian konsumen. Hasil dari penelitian ini berupa data pola pembelian/struk yang memiliki nilai confidence yang tinggi sebagai bahan untuk merekomendasi tata letak sesuai banyak barang yang paling sering dibeli. Kata Kunci --- Data Mining, Association Rules, Market Based Analysis, Java Wek