Data Mining - Concepts and Techniques.

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

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Agile manufacturing: A framework for research and development

Agile Manufacturing (AM) is a relatively new operations concept that is intended to improve the competitiveness of firms. Manufacturing/service processes based on AM are characterized by customer-supplier integrated processes for product design, manufacturing, marketing, and support services. Agile manufacturing requires enriching of the customer; cooperating with competitors; organizing to manage change, uncertainty and complexity; and leveraging people and information. In recent years, a number of research papers have been published in the area of AM. The term 'agile' was coined in 1991. However, there are still some serious concerns that prevent companies from taking an entirely different direction from AM. Considering the potential importance of agile manufacturing in 21st century manufacturing competitiveness, an attempt has been made in this paper to reexamine the scope, definitions and strategies of AM. In addition, a framework has been presented as a basis for understanding the major strategies and relevant technologies of AM.

Agile manufacturing: A taxonomy of strategic and technological imperatives

Intelligent data analysis

The generation of frequent itemsets is an essential and time-consuming step in mining association rules. Most of the studies adopt the Apriori-based approach, which has great effort in generating candidate itemsets and needs multiple database accesses. Recent studies indicate that FP-tree approach has been utilized to avoid the generation of candidate itemsets and scan transaction database only twice, but they work with more complicated data structure. Besides, it needs to adjust the structure of FP-tree when it applied to incremental mining application. It is necessary to adjust the position of an item upward or downward in the structure of FP-tree when a new transaction increases or decreases the accumulation of the item. The process of the adjustment of the structure of FP-tree is the bottlenecks of the FP-tree in incremental mining application. Therefore, algorithms for efficient mining of frequent patterns are in urgent demand.

This paper aims to improve both time and space efficiency in mining frequent itemsets and incremental mining application. We propose a novel QSD (Quick Simple Decomposition) algorithm using simple decompose principle which derived from minimal heap tree, we can discover the frequent itemsets quickly under one database scan. Meanwhile, QSD algorithm doesn't need to scan database and reconstruct data structure again when database is updated or minimum support is varied. It can be applied to on-line incremental mining applications without any modification.

Comprehensive experiments have been conducted to assess the performance of the proposed algorithm. The experimental results show that the QSD algorithm outperforms previous algorithms.

An efficient incremental mining algorithm-QSD

Concept hierarchies are important for generalization in many data mining applications. We propose a method to automatically build a concept hierarchy from a provided distance matrix. The method is a modification of traditional agglomerative hierarchical clustering algorithm. When two closest clusters are selected for combining into a new cluster, the algorithm either creates a new cluster with the two original clusters as its sub-clusters, or let a cluster join the other without creating a new cluster at the higher level of the hierarchy. For the purpose of algorithm evaluation, a distance matrix is derived from the concept hierarchy built by algorithm. Root mean squared error between the provided distant matrix and the derived distance matrix is used as evaluation criterion. Empirical results show that the traditional algorithm under complete link strategy performs better than the other strategies, our algorithms perform almost the same under the three strategies, and our algorithms perform better than the traditional algorithms under various situations.

/pdf/building-a-concept-hierarchy-by-hierarchical-clustering-with-3dza1rv35f.pdf

Building a Concept Hierarchy by Hierarchical Clustering with Join/Merge Decision

Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes.

http://www.bioinformation.net/006/97320630006010.pdf

Discovering amino acid patterns on binding sites in protein complexes.

Agile manufacturing systems can benefit significantly from a database support. This paper describes AMDS, an agile manufacturing database system designed for capturing and manipulating the operational data of a manufacturing cell. AMDS is a continuous data-gathering real-time DBMS, and it can be logged either locally or remotely and used for off-line analysis as well. The temporal operational data obtained is used for performance and reliability analysis, high-level summary report generation, real-time monitoring and active interaction. This paper gives an overview of AMDS and its real-time features.

A database server architecture for agile manufacturing

Concept hierarchies are important in many generalized data mining applications, such as multiple level association rule mining. In literature, concept hierarchy is usually given by domain experts. In this paper, we propose algorithms to automatically build a concept hierarchy from a provided distance matrix. Our approach is modifying the traditional hierarchical clustering algorithms. For the purpose of algorithm evaluation, a distance matrix is derived from the concept hierarchy built by our algorithm. Root mean squared error between the provided distant matrix and the derived distance matrix is used as evaluation criterion. We compare the traditional hierarchical clustering and our modified algorithm under three strategies of computing cluster distance, namely single link, average link, and complete link. Empirical results show that the traditional algorithm under complete link strategy performs better than the other strategies. Our modified algorithms perform almost the same under the three strategies; and our algorithms perform better than the traditional algorithms under various situations.

Huang-Cheng Kuo

Papers

An efficient incremental mining algorithm-QSD

Building a Concept Hierarchy by Hierarchical Clustering with Join/Merge Decision

Discovering amino acid patterns on binding sites in protein complexes.

A database server architecture for agile manufacturing

Building a Concept Hierarchy from a Distance Matrix