INTELLIGENT DATA MINING TECHNIQUES AND APPLICATIONS STUDIES IN COMPUTATIONAL INTELLIGENCE

Lecture Business management information system - Lecture 26: Data mining. In this chapter, the following content will be discussed: What is data mining? Why data mining? What applications? What techniques? What process? What software?

algorithms can be used as an assistant tool in clinical practice for better understanding and prevention of unwanted medical events.
In the co-evolutionary system, by utilizing the existing Internet and hardware resources, distributed computing is naturally incorpor[r]

Given the attributes A 1 ,..., A n of a database A, such methods are to find the attributes in B 1 ,..., B m of database B that describe the same concept. Such relationships can be one-to-one, many-to-one or many-to-many. The first case is when an attribute in one file corresponds to an a[r]

Models representing uncertainty factors; data mining techniques; calculation methods of power systems taking into account uncertainty factors.

recent investigations of Parkinson’s disease using MEG have utilised more specific analysis techniques which are more suitable to studying the disease than the simple ECD.
Many of the analysis methods used focus on characterising the oscillatory dynamics associated with Parkinson’s diseas[r]

xxviii Preface
whose constant support and encouragement have made our work on this edition a rewarding experience. These include Gul Agha, Rakesh Agrawal, Loretta Auvil, Peter Bajcsy, Geneva Belford, Deng Cai, Y. Dora Cai, Roy Cambell, Kevin C.-C. Chang, Sura- jit Chaudhuri, Chen Chen, Yi[r]

let S = {S 1 ,...!Sk}. These subsets are mutually exclusive and have approximately equal
size. The classifier is iteratively trained and tested k times. In iteration i, the Si subset is reserved as the test set while the remaining subsets are used to train the classifier. Then t[r]

This paper describes three building blocks of a technological Knowledge Management (KM) system that provides all relevant and practical means of supporting KM and thus differentiates itself from existing KM tools in goal and approach, as they usually deal with a limited range only. The three blocks[r]

2. Subjective knowledge : This kind of knowledge represents the linguistic informations defined through set of rules, knowledge from expert and design specifications, which are usually impossible to be described quantitatively.
Fuzzy systems are able to coordinate both types of knowledge to s[r]

of neurons. Two layers correspond respectively to the machines (called machine-type pool) and parts (called part-type pool), and one hidden layer serves as a buffer between the machine-type pool and part- type pool. Similarity coefficients of machines and parts are use[r]

Data mining primarily deals with structured data. Text mining mostly handles unstructured data/text. Web mining lies in between and copes with semi-structured data and/or unstructured data. The mining process include[r]

Mining Dense Substructures
In the analysis of graph pattern mining, researchers have found that there exists a spe- cific kind of graph structure, called a relational graph , where each node label is used only once per graph. The relational graph is widely used in model[r]

Privacy-Preserving Data Mining of Graphs. In many applications such as social networks, it is critical to preserve the privacy of the nodes in the underlying network. Simple de-identification of the nodes during the release of a network structure is not sufficient,[r]

Many sites try obscure e-mail addresses in order to fool data mining programs.This is done for a good reason: the majority of the data mining programs troll sites to collect e-mail addresses for spammers. If you want a sure fire way to receive a lot of spam, post t[r]

Finally, we introduce association-based classification, which classifies documents
based on a set of associated, frequently occurring text patterns. Notice that very frequent terms are likely poor discriminators. Thus only those terms that are not very frequent and that have good discrimi[r]

Using training data to derive a classifier or predictor and then to estimate the accuracy of the resulting learned model can result in misleading overoptimistic estimates due to overspecialization of the learning algorithm to the data. (We’ll say more on this in a[r]

Bibliographic Notes 281
a vertical database layout, was proposed in Zaki, Parthasarathy, Ogihara, and Li [ZPOL97].
Other scalable frequent itemset mining methods have been proposed as alternatives to the Apriori-based approach. FP-growth, a pattern-growth approach for m[r]

...
Figure 12.3. Branch-and-Bound Search
Algorithm 15 outlines the framework of branch-and-bound for searching the optimal graph pattern. In the initialization, all the subgraphs with one edge are enumerated first and these seed graphs are then iteratively extended to[r]

evitably limited in scope; many data-mining techniques, particularly specialized methods for particular types of data and domains, were not mentioned specifically. We believe the general discussion on data-mining tasks and components h[r]

Figure 35. Graph between ‘calculated seasonal index’ and ‘average index’ of ‘Sales’ dataset
6. Conclusion
The book chapter presents a unified data mining theory and then the mathematical formulation of the unified data mining theory (UDMT). The data[r]