Integrating Data mining With Computer Games

Submitted by :B.Sai Kiran Goud

138R1A0419

Under the guidance : Prof : T.Sathyanarayana

Contents:• INTRODUCTION• DEFINITION• BLOCK DIAGRAM• CLASSIFICATION• KEY COMPONENTS • USES OF DATA MINING• DATA MINING CONTROVERSIES• CONCLUSION• FUTURE SCOPE• REFERENCES

Abstract

Playing computer games for many years has led to a large volume of gaming data that consist of gamers’ likings and their playing behavior. Such data can be used by game creators to extract knowledge for enhancing games. Mining computer game data is a new data mining approach that can help in developing games as per a gamer’s requirements and his/her area of interest. Since the gaming industry has been contributing to the countries’ revenue on a large scale, so improvement in this industry becomes vital. This study aims to apply data mining techniques such as association, classification, and clustering for improving game design, game marketing, and game stickiness monitoring, respectively, to enrich game quality

INTRODUCTION: The gaming industry no longer remains a restricted field for a particular age group or consumer segment. With the arrival of mobile gaming and enhancements to hardware used in playing these games, gaming has become a feasible form of entertainment for players from all races and ages. This shift to mainstream has led to an increase in revenue generated by the industry with approximately 20.5 billion INR [1] and a predicted rise up to 40.6 billion INR by 2018 [2]. India ranks 20th in terms of revenue generation from the gaming industry amongst the other countries [1]. In addition, the growth and expansion of the gaming industry are due to certain enrichments in technology and hardware. Consequently, modern games have become demanding as applications and serious gamers are among those who purchase high-powered devices to keep pace with the newest games. During early ages, the existence of computer games was to a small extent, but within a few years, it has been drastically increased. Thus, a large amount of gaming data has been generated. Valuable information can be extracted from such data to improve computer games. The knowledge discovery process can be achieved through data mining techniques where the gaming data will be refined and could be utilized to improve the value of computer games. Thus, enhancing the design of games and the number of gamers fascinated toward a particular game will tend to increase exponentially.

Data Mining DEFINITION :

• Data mining is the process in which interesting knowledge and pattern are discovered from large amounts of data stored either in data warehouses, databases, or other information repositories

Pattern recognition

Artificial

intelligence

Databases

Machinelearning

Dataminin

g

Mathematicalmodeling

Block diagram

Classification of data mining functionalities

• Characterization & discrimination

• Association analysis

• Classification & Regression

• Clustering

• Evolution & deviation analysis

• Data can be associated with classes or concepts. It can be useful to describe individual classes and concepts in summarized, concise, and yet precise terms. Such descriptions of a class or a concept are called class/concept descriptions. These descriptions can be derived via (1) data characterization, by summarizing the data of the class under study (often called the target class) in general terms, or (2) data discrimination, by comparison of the target class with one or a set of comparative classes (often called the contrasting classes), or (3) both data characterization and discrimination

Characterization & discrimination

 • Association analysis is the discovery of association

rules showing attribute-value conditions that occur frequently together in a given set of data. Association analysis is widely used for market basket or transaction data analysis.

Association analysis

• Classification is the processing of finding a set of models (or functions) which describe and distinguish data classes or concepts, for the purposes of being able to use the model to predict the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (i.e., data objects whose class label is known). It is the construction & use of a model to access the class of an unlabeled object, or to access the value or value ranges of an attribute that a given attribute is likely to have

Classification & Regression

• Unlike classification and predication, which analyze class-labeled data objects, clustering analyzes data objects without consulting a known class label. In general, the class labels are not present in the training data simply because they are not known to begin with. Clustering can be used to generate such labels. The objects are clustered or grouped based on the principle of maximizing the in traclass similarity and minimizing the interclass similarity. That is, clusters of objects are formed so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Each cluster that is formed can be viewed as a class of objects, from which rules can be derived. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together

Clustering

• Data evolution analysis describes and models regularities or trends for objects whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time-related data, distinct features of such an analysis include time-series data analysis, sequence or periodicity pattern matching, and similarity-based data analysis

Evolution & deviation analysis

Key Component of Data Mining

• Whether Knowledge Discovery or Knowledge Prediction, data mining takes information that was once quite difficult to detect and presents it in an easily understandable format (ie: graphical or statistical)

• Data mining Techniques involve sophisticated algorithms, including Decision Tree Classifications, Association detection, and Clustering.

• Since Data mining is not on test, I will keep things superficial.

Uses of Data Mining

• Health and Science

Protein Folding Predicting protein interactions and functionality within biological cells. Applications of this research include determining causes and possible cures for Alzheimers, Parkinson's, and some cancers (caused by protein "misfolds")

• Extra-Terrestrial Intelligence

Scanning Satellite receptions for possible transmissions from other planets.

Data Mining Controversies

• Latest one: Facebook's Beacon Advertising program (Just popped on Slashdot within the last week)

• What Beacon does:

“when you engage in consumer activity at a [Facebook] partner website, such as Amazon, eBay, or the New York Times, not only will Facebook record that activity, but your Facebook connections will also be informed of your purchases or actions.” [taken from http://trickytrickywhiteboy.blogspot.com/2007/11/beware- of-facebooks-beacon.html]

Conclusion

• Data obtained through Data Mining is incredibly valuable

• Companies are understandably reluctant to give up data they have obtained. • Expect to see prevalence of Data Mining and (possibly subversive) methods increase in years to come.

Future scope

•  Relational database is adopted by many as a main stream database. The scope of data warehousing and data mining is very good. There are many tools used for data warehousing but the most accepted ETL tool is Informatical.

References

Wikipedia Data Mining entry

http://en.wikipedia.org/wiki/Data_mining

Facebook's Faux Pas

http://www.newsweek.com/id/69275

Beware of Facebook’s Beacon

http://trickytrickywhiteboy.blogspot.com/2007/11/beware-of-facebooks-beacon.html