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.

“Bioinformatics” 특집을 내면서

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

Matrix Factorization Techniques for Recommender Systems

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

A top-k OLAP query groups measures with respect to some abstraction level of interesting dimensions and selects the k groups with the highest aggregate value. An example of such a query is “find the 10 combinations of product-type and month with the largest sum of sales”. Such queries may also be applied in a spatial database context, where objects are augmented with some measures that must be aggregated according to a spatial division. For instance, consider a map of objects (e.g., restaurants), where each object carries some non-spatial measure (e.g., the number of customers served during the last month). Given a partitioning of the space into regions (e.g., by a regular grid), the goal is to find the regions with the highest number of served customers. A straightforward method to evaluate a top-k OLAP query is to compute the aggregate value for each group and then select the groups with the highest aggregates. In this paper, we study the integration of the top-k operator with the aggregate query processing module. For this, we make use of spatial indexes, augmented with aggregate information, like the aggregate R–tree. We device a branch-and-bound algorithm that accesses a minimal number of tree nodes in order to compute the top-k groups. The efficiency of our approach is demonstrated by experimentation.

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Evaluation of top-k OLAP queries using aggregate r–trees

Nearest Neighbor Queries in Network Databases.

Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are inadequately interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking covers this gap with batch techniques that are restricted to individual topological relations, even though most operations are common for all main relations. In this work, we introduce a batch algorithm that simultaneously computes all topological relations and define the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. We propose two progressive algorithms and conduct a thorough experimental study over large, real datasets, demonstrating the superiority of our techniques over the current state-of-the-art.

Progressive, Holistic Geospatial Interlinking

A column (10) of a memory array includes a plurality of memory cells (11, 12) each having first and second independent access ports (T1, T2) and a cross-coupled memory latch (20). The first access port (T1) of each memory cell (11, 12) connects a first node (21) of the latch (20) to a first bit line (14), while the second access port (T2) of each memory cell connects a second node (22) of the latch (20) to a second bit line (15). A clearing arrangement (T7) is connected to the second bit line (15) for selectively coupling the second bit line to ground. A write driver is connected to the first bit line (14) for writing data to the memory cells (11, 12) in the form of single-ended signals. A memory cell is placed in a preset condition by simultaneously coupling the second node (22) to the second bit line (15) through the second access port (T2) and coupling the second bit line to ground through the clearing arrangement (T7). Once in the preset condition, data may be written to the cell by coupling the first bit line (14) to the first node (21) through the first access port (T1) and driving data to the first bit line.

Conceptual Title Abstractions: Modeling and Querying Very Large Interactive Multimedia Repositories.

Spatial objects associated with keywords are prevalent in applications such as Google Maps and Twitter. Recently, the topic of spatial keyword queries has received plenty of attention. Spatial Group Keyword (SGK) search is a popular class of queries; their goal is to find a set of objects which are close to each other and are associated to a set of input keywords. In this paper, we propose SpaceKey, a system for retrieving and visualizing spatial objects returned by SGK queries. In addition to existing SGK query types, SpaceKey supports a novel query, called SPM query. An SPM query is defined by a spatial pattern, a graph whose vertices contain keywords and its edges are associated with distance constraints. The results are sets of objects that match the pattern. SpaceKey allows users to perform comparison analysis between different SGK query types. We plan to make SpaceKey an open-source web-based platform, and design API functions for software developers to plug other SGK query algorithms into our system.

Nikos Mamoulis

Papers

Evaluation of top-k OLAP queries using aggregate r–trees

Nearest Neighbor Queries in Network Databases.

Progressive, Holistic Geospatial Interlinking

Conceptual Title Abstractions: Modeling and Querying Very Large Interactive Multimedia Repositories.

SpaceKey: Exploring Patterns in Spatial Databases