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

DISCOVERING PATTERNS FROM TEMPORAL DATABASES USING TEMPORAL ASSOCIATION RULE

Data mining is the process of discovering and examining data from diverse viewpoint, using automatic or semiautomatic techniques to remove knowledge or useful information and discover correlations or meaningful patterns and rules from large databases. One of the most vital characteristic missed by the traditional data mining systems is their capability to record and process time-varying aspects of the real world databases. . Temporal data mining, which mines or discovers knowledge and patterns from temporal databases, is an extension of data mining with capability to include time attribute analysis. The pattern discovery task of temporal data mining discovers all patterns of interest from a large dataset. This paper presents an overview of temporal data mining and focus on pattern discovery using temporal association rules

An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications

This paper surveys the growing number of indu5 trial applications of data mining and knowledge discovery. We look at the existing tools, describe some representative applications, and discuss the major issues and problems for building and deploying successful applications and their adoption by business users. Finally, we examine how to assess the potential of a knowledge discovery application.

Concept discovery innovations in law enforcement: a perspective.

In the past decades, the amount of information available to law enforcement agencies has increased significantly. Most of this information is in textual form, however analyses have mainly focused on the structured data. In this paper, we give an overview of the concept discovery projects at the Amsterdam-Amstelland police where Formal Concept Analysis (FCA) is being used as text mining instrument. FCA is combined with statistical techniques such as Hidden Markov Models (HMM) and Emergent Self Organizing Maps (ESOM). The combination of this concept discovery and refinement technique with statistical techniques for analyzing high-dimensional data not only resulted in new insights but often in actual improvements of the investigation procedures.Formal concept analysis; Intelligence led policing; Knowledge discovery;

A Survey on Image Mining Techniques: Theory and Applications

Image mining is a vital technique which is used to mine knowledge straightforwardly from image. Image segmentation is the primary phase in image mining. Image mining is simply an expansion of data mining in the field of image processing. Image mining handles with the hidden knowledge extraction, image data association and additional patterns which are not clearly accumulated in the images. It is an interdisciplinary field that integrates techniques like computer vision, image processing, data mining, machine learning, data base and artificial intelligence. The most important function of the mining is to generate all significant patterns without prior information of the patterns. Rule mining has been adopting to huge image data bases. Mining has been done in accordance with the integrated collections of images and its related data. Numerous researches have been carried on this image mining. This paper presents a survey on various image mining techniques that were proposed earlier in literature. Also, this paper provides a marginal overview for future research and improvements. Keywords— Data Mining, Image Mining, Knowledge Discovery, Segmentation, Machine Learning, Artificial Intelligence, Rule Mining, Datasets

Knowledge Discovery in Databases: An Information Retrieval Perspective

The current trend of increasing capabilities in data generation and collection has resulted in an urgent need for data mining applications, also called knowledge discovery in databases. This paper identifies and examines the issues involved in extracting useful grains of knowledge from large amounts of data. It describes a framework to categorise data mining systems. The author also gives an overview of the issues pertaining to data pre processing, as well as various information gathering methodologies and techniques. The paper covers some popular tools such as classification, clustering, and generalisation. A summary of statistical and machine learning techniques used currently is also provided

On the impact of Knowledge Discovery and Data Mining

Knowledge Discovery and Data Mining are powerful automated data analysis tools and they are predicted to become the most frequently used analytical tools in the near future. The rapid dissemination of these technologies calls for an urgent examination of their social impact. This paper identifies social issues arising from Knowledge Discovery (KD) and Data Mining (DM). An overview of these technologies is presented, followed by a detailed discussion of each issue. The paper's intention is to primarily illustrate the cultural context of each issue and, secondly, to describe the impact of KD and DM in each case. Existing solutions specific to each issue are identified and examined for feasibility and effectiveness, and a solution that provides a suitably contextually sensitive means for gathering and analysing sensitive data is proposed and briefly outlined. The paper concludes with a discussion of topics for further consideration

Post-processing of association rules.

In this paper, we situate and motivate the need for a post-processing phase to the association rule mining algorithm when plugged into the knowledge discovery in databases process. Major research effort has already been devoted to optimising the initially proposed mining algorithms. When it comes to effectively extrapolating the most interesting knowledge nuggets from the standard output of these algorithms, one is faced with an extreme challenge, since it is not uncommon to be confronted with a vast amount of association rules after running the algorithms. The sheer multitude of generated rules often clouds the perception of the interpreters. Rightful assessment of the usefulness of the generated output introduces the need to effectively deal with different forms of data redundancy and data being plainly uninteresting. In order to do so, we will give a tentative overview of some of the main post-processing tasks, taking into account the efforts that have already been reported in the literature.

Data Mining Approach for Amino Acid Sequence Classification

Computerized applications are employed all around the world, an enormous amount of data is collected. The essential information contained in large amounts of data is attracting scholars from a variety of disciplines to examine how to extract the hidden knowledge inside them. The technique of obtaining or mining usable and valuable knowledge from enormous amounts of data is known as data mining. Text mining, picture mining, sequential pattern mining, web mining, and so on are all examples of data mining fields. Sequencing mining is one of the most important technologies in this field, as it aids in the discovery of sequential connections in data. Sequence mining is used in a variety of applications, including customers' buying trends analysis, web access trends analysis, atmospheric observation, amino acid sequences, Gene sequencing, and so on. Sequence mining techniques are utilized in protein and DNA analysis for sequence alignment, pattern searching, and pattern categorization. Researchers are exhibiting an interest in the subject of amino acid sequence categorization in the field of amino acid sequence analysis. It has the ability to find recurrent patterns in homologous proteins. This study describes the numerous methods used by numerous studies to categories proteins and gives an overview of the most important sequence classification techniques