MITS5509 Assignment 1: Data Mining Introduction Presentation

This presentation provides a comprehensive introduction to data mining, a crucial technique for extracting valuable insights from large datasets to support future decision-making. It outlines the essential steps involved in the data mining process, including selection, cleaning, integration, mining, transformation, and knowledge representation. The presentation explores various algorithms and techniques used in data mining, such as classification, clustering, neural networks, association rule mining, and prediction. It categorizes data mining systems based on data types, data models, and mining techniques. Furthermore, the presentation addresses key issues related to data mining, such as user interface challenges, security concerns, and performance considerations. The conclusion emphasizes the significance of data mining in diverse sectors for informed decision-making and forecasting, acknowledging its ongoing research and development. The presentation references several academic papers to support the information provided and covers the different categories of data mining processes. This presentation is created by a student and is available on Desklib.

DATA MINING

INTRODUCTION
 The concept of data mining is mainly used for retrieving data from huge
databases in order to analyze them for predicting future decisions.
 Various algorithms are used for the purpose of data mining.

STEPS OF MINING DATA
Various steps are used for the data mining processes such as –
 Selection
 Cleaning
 Integration
 Mining
 Transformation
 Knowledge representation

USED ALGORITHMS AND TECHNIQUES
There are different techniques as well as algorithms that are used in mining
purpose are –
 Classification
 Clustering
 Neural Networks
 Association Rule
 Prediction

CATEGORY OF DATA MINING SYSTEM
The various categories involved in the system of data mining sre stated
below–
 According to data mining types
 According to different data models
 According to the various mining techniques

ISSUES THAT ARE RELATED TO DATA
MINING
The field of data mining is still under research and development and
thus the following risks might be associated with mining of data -
 Issues in user interface
 Security issues
 Performance issues

CONCLUSION
The data mining technique is used in various sectors for decision making
and predicting and the field of mining is currently under research. The
various techniques of data mining and algorithms and also the issues that
are related to the mining techniques are illustrated in the presentation. The
various categories of the data mining process are also described above.
Thus it can be concluded that the mining process plays an important role in
prediction and future decision making.

REFERENCES
 [1] P.N. Tan, Introduction to data mining. Pearson Education India, 2018.
 [2] I.H. Witten, E. Frank, M.A. Hall and C.J. Pal, Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann, 2016.
 [3] D.T. Larose and C.D. Larose. Discovering knowledge in data: an introduction to
data mining. John Wiley & Sons, 2014.
 [4] C.C. Aggarwal, Data mining: the textbook. Springer, 2015.
 [5] A.A. Freitas. Data mining and knowledge discovery with evolutionary algorithms.
Springer Science & Business Media, 2013.
 [6] J.A. Silva, E.R. Faria, R.C. Barros, E.R. Hruschka, A.C. De Carvalho and J. Gama.
Data stream clustering: A survey. ACM Computing Surveys (CSUR), 46(1), 2013,
p.13.
 [7] G. Kesavaraj and S. Sukumaran. A study on classification techniques in data
mining. In 2013 Fourth International Conference on Computing, Communications
and Networking Technologies (ICCCNT). IEEE, July, 2013, pp. 1-7.
 [8] S. Poria, E. Cambria and A. Gelbukh. Aspect extraction for opinion mining with a
deep convolutional neural network. Knowledge-Based Systems, 108, 2016, pp.42-49.

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