Sai kiran goud sem.ppt

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
Data
bases
Machine
learning
Data
mining
Mathematical
modeling

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

Sai kiran goud sem.ppt

More Related Content

What's hot (19)

Viewers also liked (7)

Similar to Sai kiran goud sem.ppt (20)

Recently uploaded (20)

Sai kiran goud sem.ppt