ITEC 4230 Data Analyst Assignment 1a - York University

ITEC 4230 Data Analyst Exam - York University Q (1) What is data mining? In your answer, address the following: (a) Is it another hype? Data mining is the process of discovering interesting patterns from massive amounts of data. As a knowledge discovery process, it typically involves data cleaning, data integration, data selection, data transformation, pattern discovery, pattern evaluation, and knowledge presentation. Data mining is not a hype. Loads of data which was up until now not used and utilized can be made efficient with the aid of data mining. Data mining give value and meaning to otherwise useless data. (b) Is it a simple transformation or application of technology developed from databases, statistics, machine learning, and pattern recognition? No. Data mining is more than a simple transformation of technology developed from databases, statistics, and machine learning. Instead, data mining involves an integration, rather than a simple transformation, of techniques from multiple disciplines such as database technology, statistics, machine learning, high-performance computing, pattern recognition, neural networks, data visualization, information retrieval, image and signal processing, and spatial data analysis. (c) We have presented a view that data mining is the result of the evolution of database technology. Do you think that data mining is also the result of the evolution of machine learning research? Can you present such views based on the historical progress of this discipline? Address the same for the fields of statistics and pattern recognition. Machine learning investigates how computers can learn (or improve their performance) based on data. A main research area is for computer programs to automatically learn to recognize complex patterns and make intelligent 2 decisions based on data. For example, a typical machine learning problem is to program a computer so that it can automatically recognize handwritten postal codes on mail after learning from a set of examples. Machine learning is a fast-growing discipline. I believe data mining is the result of the evolution of machine learning in the following ways- 1.Supervised learning 2. Unsupervised learning 3. Semi-supervised learning 4. Active learning Statistics studies the collection, analysis, interpretation or explanation, and presentation of data. Data mining has an inherent connection with statistics. A statistical model is a set of mathematical functions that describe the behavior of the objects in a target class in terms of random variables and their associated probability distributions. Statistical models are widely used to model data and data classes. For example, in data mining tasks like data characterization and classification, statistical models of target classes can be built. In other words, such statistical models can be the outcome of a data mining task. Alternatively, data mining tasks can be built on top of statistical models. For example, we can use statistics to model noise and missing data values. Then, when mining patterns in a large data set, the data mining process can use the model to help identify and handle noisy or missing values in the data. Statistics research develops tools for prediction and forecasting using data and statistical models. Statistical methods can be used to summarize or describe a collection of data. Statistics is useful for