Knowledge Discovery using Various Multimedia Data Mining Technique

Knowledge discovery in databases (KDD) is the process of discovering positive information from a gathering of data. This generally used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior information on data sets and interpreting perfect solutions from the observed results. Knowledge Discovery in Databases is the process of finding knowledge in huge amount of data where data mining is the core of this process. Data mining can be used to understandable meaningful patterns from huge databases and these patterns may be transformed into knowledge. Multimedia data mining can be defined as the process of finding motivating patterns from media data such as audio mining , video mining, image mining and text mining that are not generally available by basic queries and associated results. It is the mining of knowledge and high level multimedia information from large multimedia database system. Multimedia data mining refers to sample discovery, rule extraction and knowledge gaining from multimedia database. In this paper, An Overview On various Multimedia Data technique is given and the main focus is given to the video Data Mining. DOI: 10.17762/ijritcc2321-8169.15035

Visual Systems for Interactive Exploration and Mining of Large-Scale Neuroimaging Data Archives

While technological advancements in neuroimaging scanner engineering have improved the efficiency of data acquisition, electronic data capture methods will likewise significantly expedite the populating of large-scale neuroimaging databases. As they do and these archives grow in size, a particular challenge lies in examining and interacting with the information that these resources contain through the development of compelling, user-driven approaches for data exploration and mining. In this article, we introduce the informatics visualization for neuroimaging (INVIZIAN) framework for the graphical rendering of, and dynamic interaction with the contents of large-scale neuroimaging data sets. We describe the rationale behind INVIZIAN, detail its development, and demonstrate its usage in examining a collection of over 900 T1-anatomical magnetic resonance imaging (MRI) image volumes from across a diverse set of clinical neuroimaging studies drawn from a leading neuroimaging database. Using a collection of cortical surface metrics and means for examining brain similarity, INVIZIAN graphically displays brain surfaces as points in a coordinate space and enables classification of clusters of neuroanatomically similar MRI images and data mining. As an initial step toward addressing the need for such user-friendly tools, INVIZIAN provides a highly unique means to interact with large quantities of electronic brain imaging archives in ways suitable for hypothesis generation and data mining

Multidimensional Data Visual Exploration by Interactive Information Segments

Visualization techniques provide an outstanding role in KDD process for data analysis and mining. However, one image does not always convey successfully the inherent information from high dimensionality, very large databases. In this paper we introduce VSIS (Visual Set of Information Segments), an interactive tool to visually explore multidimensional, very large, numerical data. Within the supervised learning, our proposal approaches the problem of classification by searching of meaningful intervals belonging to the most relevant attributes. These intervals are displayed as multi–colored bars in which the degree of impurity with respect to the class membership can be easily perceived. Such bars can be re–explored interactively with new values of user–defined parameters. A case study of applying VSIS to some UCI repository data sets shows the usefulness of our tool in supporting the exploration of multidimensional and very large data

Advanced data mining in field ion microscopy

Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM

Visual Analytics of High-dimensional Data Sets: A Hyperspectral Imagery Test Case

Visualization and interpretation of big data poses new and unique challenges. As engineering students enter the work force, many will be tasked with analyzing increasingly large and complex data sets with which they have little experience. This paper presents simple heat map and multi-line plotting techniques used to select critical spectral attributes produced from data mining a hyperspectral satellite image for bathymetry mapping. Additionally, good graphic design practices regarding color choice and reducing visual distraction are suggested in order to more quickly and clearly communicate information to an audience. These techniques can be applied to all types of data visualization as an effective way of communicating data

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

BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis

In the biomedical domain, taxonomies organize the acquisition modalities of scientific images in hierarchical structures. Such taxonomies leverage large sets of correct image labels and provide essential information about the importance of a scientific publication, which could then be used in biocuration tasks. However, the hierarchical nature of the labels, the overhead of processing images, the absence or incompleteness of labeled data, and the expertise required to label this type of data impede the creation of useful datasets for biocuration. From a multi-year collaboration with biocurators and text-mining researchers, we derive an iterative visual analytics and active learning strategy to address these challenges. We implement this strategy in a system called BI-LAVA Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis. BI-LAVA leverages a small set of image labels, a hierarchical set of image classifiers, and active learning to help model builders deal with incomplete ground-truth labels, target a hierarchical taxonomy of image modalities, and classify a large pool of unlabeled images. BI-LAVA's front end uses custom encodings to represent data distributions, taxonomies, image projections, and neighborhoods of image thumbnails, which help model builders explore an unfamiliar image dataset and taxonomy and correct and generate labels. An evaluation with machine learning practitioners shows that our mixed human-machine approach successfully supports domain experts in understanding the characteristics of classes within the taxonomy, as well as validating and improving data quality in labeled and unlabeled collections.Comment: 15 pages, 6 figure