RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.Comment: 10 pages, 7 figures, accepted for oral presentation at KDD '1

Unsupervised Extraction of Representative Concepts from Scientific Literature

This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the first phase of our algorithm we propose PhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-specific features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract fine grained concept mentions from the aspect-typed phrases without the need for any external resources or human effort, in a purely data-driven manner. We apply our technique to study literature from diverse scientific domains and show significant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.Comment: Published as a conference paper at CIKM 201

Towards a parallel image mining system

El análisis de imágenes puede revelar información útil para los usuarios El significativo aumento del uso de imágenes en diferentes campos de la ciencia, medicina, negocios, etc., requiere de mayor poder de procesamiento. Con el avance en la adquisición de dato multimedial y de técnicas de almacenamiento, la necesidad de descubrir automáticamente conocimiento de grandes colecciones de imágenes aumenta. La minería de imágenes, área de investigación relativamente nueva y prometedora, trata de facilitar este trabajo proponiendo soluciones para la extracción de patrones significativos y potencialmente útiles a partir de grandes volúmenes de datos. Comprende diferentes etapas demandantes de recursos y de tiempo computacional. El uso de computación paralela representa un buen punto de partida. El proceso de minería de imágenes parece ser algorítmicamente complejo, requiriendo niveles de poder computacional que solamente los paradigmas paralelos pueden proveer. Dado que involucra conjuntos de datos de rápido crecimiento y las imágenes representan una fuente natural de paralelismo, el paralelismo puede manejar semejante colección en forma efectiva. En este trabajo examinamos el problema de la minería de imágenes y su costo computacional, proponemos una posible solución global y local y definimos futuras extensiones para la minería de imágenes paralela.Images can reveal useful information to human users when are analyzed. The explosive growth in applying images as data in many fields of science, business, medicine, etc, demands greater processing power. With the advances in multimedia data acquisition and storage techniques, the need for automatically discovering knowledge from large image collections is becoming more and more relevant. Image mining, a relatively new and very promising field of investigation, tries to ease this problem proposing some solutions for the extraction of significant and potentially useful patterns from these tremendous data volume. This research field implies different stages, most of them demanding so many resources and computational time. The use of parallel computation is a good starting-point. Image mining process appears to be algorithmically complex requiring computing power levels that only parallel paradigms can provide in a timely way. As data sets involved are large, rapidly growing larger and images provide a natural source of parallelism, parallels computers could be organized to handle such big collection effectively. At this work we will examine the image mining problem with its computational cost, propose a possible global or local parallel solution and also identify some future research directions for image mining parallelism.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI