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 Warehouse And Data Mining – Neccessity Or Useless Investment

The organization has optimized databases which are used in current operations and also used as a part of decision support. What is the next step? Data Warehouses and Data Mining are indispensable and inseparable parts for modern organization. Organizations will create data warehouses in order for them to be used by business executives to take important decisions. And as data volume is very large, and a simple filtration of data is not enough in taking decisions, Data Mining techniques will be called on. What must an organization do to implement a Data Warehouse and a Data Mining? Is this investment profitable (especially in the conditions of economic crisis)? In the followings we will try to answer these questions.database, data warehouse, data mining, decision, implementing, investment

An Efficient Algorithm for Frequent Pattern Mining for Real-Time Business Intelligence Analytics in Dense Datasets

Finding frequent patterns from databases has been the most time consuming process in data mining tasks, like association rule mining. Frequent pattern mining in real-time is of increasing thrust in many business applications such as e-commerce, recommender systems, and supply-chain management and group decision support systems, to name a few. A plethora of efficient algorithms have been proposed till date, among which, vertical mining algorithms have been found to be very effective, usually outperforming the horizontal ones. However, with dense datasets, the performances of these algorithms significantly degrade. Moreover, these algorithms are not suited to respond to the real-time need. In this paper, we describe BDFS(b)-diff-sets, an algorithm to perform real-time frequent pattern mining using diff-sets and limited computing resources. Empirical evaluations show that our algorithm can make a fair estimation of the probable frequent patterns and reaches some of the longest frequent patterns much faster than the existing algorithms.

A Survey on Data Mining Algorithm for Market Basket Analysis

Association rule mining identifies the remarkable association or relationship between a large set of data items. With huge quantity of data constantly being obtained and stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis

Development of a strategic and tactical game plan for junior mining companies

ABSTRACT This thesis undertakes to present a game plan for junior mining companies. It culminates with a game plan model that incorporates all the key steps and tasks that mining investors and entrepreneurs need to use to establish globally competitive junior mining companies. It is based on the research of several listed junior mining companies. The game of junior mining is defined by rules, the player, the elements of playing, and the definitions of winning and scoring. The rules of the game are those defined by the resources industry and general business concepts. The mining assets around which the junior mining game is played are exploration projects, feasibility studies, mine development projects or operating mines. The player is the junior mining company intent on winning the game. Playing the game is done through executing the steps, tasks and simple models and matrices associated with the four business pillars: strategy development, legal and financial, operations management and risks management. The first pillar is strategic, while the last three pillars are tactical. For each business pillar, databases were developed, for the purposes of creating references and benchmarks for the game plan. The databases have been created from the analysis of twenty randomly selected junior mining companies, the author’s practical experiences and previous research. Scoring the game is undertaken by completing the game score matrix, which scores the mining assets, the business pillars and the financial performance, and provides an overall company score. The total company score highlights the strengths and weaknesses of the company. Undertaking the process of playing the game iteratively will lead to creating a globally competitive junior mining company. Winning the game is defined as creating a sustainable junior mining company, that grows to a mid-tier company, or is bought out by a major mining company. ii In the thesis, the game is played using the hypothetical case study of a of a coal junior mining company, with a marginal coal mine, an attractive feasibility study and an exploration project. Step by step the game is played leading to a company score and exposing the company’s strengths and weaknesses. The research concludes by presenting a holistic game plan model that can be applied to any junior mining company in any commodity in constantly changing resources industry dynamics

Database Marketing In Travel And Tourism

An increasing number of organisations are developing customer databases in a bid to get closer to their customers and gain competitive advantage. This report investigates the practice of database marketing among different travel and tourism sectors, including airlines, hotels, museums and tour operators, and draws on UK and international examples. It compares direct marketing and database marketing and examines the different levels of sophistication at which database marketing can be practiced, the role of customer loyalty schemes, the ways in which a database can be segmented, the role of consumer data profiling companies and current developments in database marketing. The use of database marketing for customer retention and business acquisition is also investigated. In order to ensure true customer relationship building it is vital for the industry to leverage the information on their databases and provide customer recognition through the delivery of personalised service. Business acquisition through customer retention is likely to be a key strategy in future through the use of data-mining and cross-selling techniques. The report concludes that organisations must create a new marketing environment by moving away from transaction marketing towards the principles of customer relationship management

Neural Networks in Data Mining

Data Mining means extraction of hidden predictive information from huge amount of databases. It is beneficial in every field like business, engineering, web data etc. In data mining classification of data is very difficult task that can be solving by using different algorithms. The more common model functions in data mining include classification, clustering, rule generation and knowledge discovery. There are many technologies available to data mining, including Artificial Neural Networks, Regression, and Decision Trees. In this paper the data mining based on neural networks is studied in detail, and the key technology and ways to achieve the data mining based on neural networks are also studied