A Review on Business Intelligence and Big Data <s> Big Data Mining <s> Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest. <s> BIB001 </s> A Review on Business Intelligence and Big Data <s> Big Data Mining <s> This chapter introduces the foundations of machine learning that are necessary to motivate its combination to evolutionary algorithms and image processing. In the chapter, we will describe several machine learning tools, suitable for a wide variety of data and tasks. The goal is not to describe the total number of techniques, but instead to present a general view of the field through the description of the mail problems considered in machine learning. <s> BIB002
Big data mining can be defined same as classical data mining, the process of a set of techniques and methods to extract valuable information from data or interesting, unexpected, and unknown patterns in data BIB001 , to make better predictions and decisions. Alpaydın BIB002 also defines data mining as: "Application of machine learning methods to large databases". The difference between big data and classical data mining is the data characteristics (volume, variety etc.) on which they run. Data mining tries to give answers to the following kind of questions: What is in the data? What kind of patterns are hidden in the data? And how an organization can benefit from these patterns and relationships in the mess of data? BIB001 . As seen from the definition above data mining is a process, defined by Cross Industry Standard Process for Data Mining (CRISP-DM) which is a process to pioneer data mining exertions  . CRISP-DM overviews data mining life cycle shown in Figure 2 . Business understanding is simply to understand the problem which can be sometimes simple and sometimes vague. Data understanding phase deals with raw material on which problem solving is based. Data preparation is the phase of getting data appropriate format, structure and quality to yield better results. Because most analytic tools require such a necessity. Modelling is the process of capturing the pattern in the data. Evaluation assesses the result whether they are consistent or reliable. Deployment is the use of data mining techniques in real world cases.