Data Mining

1. Data Mining What is Data Mining? Data mining is the analysis step of Knowledge Discovery in databases also known as KDD. Data mining software is one of a number of analytical tools for analysing data. It allows users to analyse data from many different dimensions or angles, categorise it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. • How is DM used in Computer Games? • DM is used in computer games by searching through historical data collected and applying DM techniques in order to locate patterns or trends. This will show strengths or weakness allowing the gaming studios to improve on their weaker areas. • Example of DM used in computer games. • In the PC game Battlefield 4, game studios use data mining to check through their gathered data locating important information, such as how many times a game has been paused e.g. (game paused at 13:12 for 20 mins). Using this information they can see how many players come back to resume the game which shows how addictive the game produced is and compare it with battlefield 3.

2. Text Analytics What is Text Analytics ? Text analytics refers to the process of deriving important information from text which is typically derived from devising patterns or trends. Text analytics can also be referred as text data mining. • How is TA applied in Computer Games? • Text analytics is often used in computer games in order to assess how the game is played by different users. This allows the game studios to recognise patterns or trends in game play, with this information analysts can recommend room for improvement. • Example of TA used in Computer Games • Fifa 13 uses text analytics's to observe the historical data of real football players. Then locate patterns or trends in their history and implement them on to computer games giving each player the realist attributes and history. For example: Robin Van Persie, foot, age, gender, goals scored by headers etc

3. DM Methods Description: is when analysts are simply trying to describe patterns of trends in game data. Characterization: is simply the summation of general features of objects in a target class (or sample), producing a characteristic rule. Discrimination: is when features of objects across two classes (or samples) is compared. Classification: is used to organize data into classes, which is hugely useful to game development. Estimation: is similar to classification, but the target variable is numerical, not categorical. Prediction: is reminiscent of classification and estimation, but with prediction, we want to know about the future. Clustering: is a lot like classification, in that the aim is to order data into classes. Association (affinity): when performing an association analysis, the goal is to find features (attributes) that “go together”, thus defining association rules in the data.

4. Practical Issues in Game DM & TA Transparency: The patterns discovered by data mining tools are useful only if they are interesting and understandable to the targeted user/audience. Data cleaning: Data analysis which can be as good as the data that is being analysed, and most algorithms assume the data to be noise-free. Performance and sampling: Many methods for data analysis and interpretation were not originally designed for the very large datasets that exist today. Security: Is an important issue with any game telemetry data collection, whether intended for low-level work or strategic decision making. Game telemetry data are generally considered confidential in the industry, and should be kept safe. Social and privacy issues: One of the key issues in Data Mining is the question of individual privacy. Collection strategies: There are two fundamental ways to obtain data from an installed game client or hardware unit e.g. Xbox 360, PS3, PSP, smart phone

5. Pros & Cons Of DM and TA

6. Conclusion Data mining is key in order to observe information and locate high-quality information. Computer Games use data mining to search through historical data; locate patterns and trends. It's a beneficial tool that helps many companies make efficient decisions by analaysing the data. As with anything practical issues are there but the benefits outweigh the risks.