Video Data Mining

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.

Download Full-text

Video Data Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch312 ◽

2011 ◽

pp. 2042-2047

Author(s):

JungHwan Oh

Keyword(s):

Data Mining ◽

Research Area ◽

Multimedia Databases ◽

Video Data ◽

Multimedia Data ◽

Data Sets ◽

Data Set ◽

Useful Knowledge ◽

Active Research ◽

Diverse Data

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.

Download Full-text

A Distributed Content-Based Video Retrieval System for Large Data-sets

10.21203/rs.3.rs-255106/v1 ◽

2021 ◽

Author(s):

ElMehdi SAOUDI ◽

Said Jai Andaloussi

Keyword(s):

Real Time ◽

Retrieval System ◽

Video Retrieval ◽

Multimedia Databases ◽

Video Data ◽

Multimedia Data ◽

Machine Learning Techniques ◽

Data Sets ◽

Data Set ◽

Content Based Video Retrieval

Abstract With the rapid growth of the volume of video data and the development of multimedia technologies, it has become necessary to have the ability to accurately and quickly browse and search through information stored in large multimedia databases. For this purpose, content-based video retrieval ( CBVR ) has become an active area of research over the last decade. In this paper, We propose a content-based video retrieval system providing similar videos from a large multimedia data-set based on a query video. The approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key-frames for rapid browsing and efficient video indexing. We have implemented the proposed approach on both, single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments are performed using various benchmark action and activity recognition data-sets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to state-of-the-art methods.

Download Full-text

AN OPTIMIZED ARM SCHEME FOR DISTINCT NETWORK DATA SET

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2015.1302 ◽

2015 ◽

pp. 191-195

Author(s):

K.GANESH KUMAR ◽

H.VIGNESH RAMAMOORTHY ◽

M.PREM KUMAR ◽

S. SUDHA

Keyword(s):

Data Mining ◽

Association Rule ◽

Association Rule Mining ◽

Distributed Databases ◽

Research Area ◽

Sequential Algorithm ◽

Data Sets ◽

Rule Mining ◽

Data Set ◽

Communication Costs

Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.

Download Full-text

MANAGING AND MINING MULTIMEDIA DATABASES

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213004001776 ◽

2004 ◽

Vol 13 (03) ◽

pp. 739-759 ◽

Cited By ~ 3

Author(s):

BHAVANI THURAISINGHAM

Keyword(s):

Data Mining ◽

Multimedia Databases ◽

Video Data ◽

Active Research

Several advances have been made on managing multimedia databases as well as on data mining. Recently there is active research on mining multimedia databases. This paper provides an overview of managing multimedia databases and then describes issues on mining multimedia databases. In particular mining text, image, audio and video data are discussed.

Download Full-text

Multimedia data mining and retrieval for multimedia databases using associations and correlations

ACM SIGMultimedia Records ◽

10.1145/2069196.2069200 ◽

2011 ◽

Vol 3 (1) ◽

pp. 21-22 ◽

Cited By ~ 1

Author(s):

Lin Lin

Keyword(s):

Data Mining ◽

Multimedia Databases ◽

Multimedia Data ◽

Multimedia Data Mining

Download Full-text

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

Advances in Data Mining and Database Management - Biologically-Inspired Techniques for Knowledge Discovery and Data Mining ◽

10.4018/978-1-4666-6078-6.ch008 ◽

2014 ◽

pp. 174-203 ◽

Cited By ~ 4

Author(s):

Miroslav Hudec ◽

Miljan Vučetić ◽

Mirko Vujošević

Keyword(s):

Data Mining ◽

Fuzzy Logic ◽

Relational Databases ◽

Missing Values ◽

Expert Knowledge ◽

Real Data ◽

Research Area ◽

Functional Dependencies ◽

Useful Knowledge ◽

Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

Download Full-text

Finding Persistent Strong Rules

Knowledge Discovery Practices and Emerging Applications of Data Mining - Advances in Data Mining and Database Management ◽

10.4018/978-1-60960-067-9.ch005 ◽

2010 ◽

pp. 85-107

Author(s):

Anthony Scime ◽

Karthik Rajasethupathy ◽

Kulathur S. Rajasethupathy ◽

Gregg R. Murray

Keyword(s):

Data Mining ◽

Association Rules ◽

Strong Association ◽

National Election ◽

Data Sets ◽

Rule Discovery ◽

Discovery Process ◽

Data Set ◽

Rule Sets ◽

Election Studies

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

Download Full-text

Audio and Speech Processing for Data Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch017 ◽

2011 ◽

pp. 98-103 ◽

Cited By ~ 1

Author(s):

Zheng-Hua Tan

Keyword(s):

Data Mining ◽

Speech Processing ◽

Large Data ◽

Data Preprocessing ◽

Multimedia Data ◽

Data Sets ◽

Data Types ◽

Multimedia Data Mining ◽

Customer Preferences ◽

And Storage

The explosive increase in computing power, network bandwidth and storage capacity has largely facilitated the production, transmission and storage of multimedia data. Compared to alpha-numeric database, non-text media such as audio, image and video are different in that they are unstructured by nature, and although containing rich information, they are not quite as expressive from the viewpoint of a contemporary computer. As a consequence, an overwhelming amount of data is created and then left unstructured and inaccessible, boosting the desire for efficient content management of these data. This has become a driving force of multimedia research and development, and has lead to a new field termed multimedia data mining. While text mining is relatively mature, mining information from non-text media is still in its infancy, but holds much promise for the future. In general, data mining the process of applying analytical approaches to large data sets to discover implicit, previously unknown, and potentially useful information. This process often involves three steps: data preprocessing, data mining and postprocessing (Tan, Steinbach, & Kumar, 2005). The first step is to transform the raw data into a more suitable format for subsequent data mining. The second step conducts the actual mining while the last one is implemented to validate and interpret the mining results. Data preprocessing is a broad area and is the part in data mining where essential techniques are highly dependent on data types. Different from textual data, which is typically based on a written language, image, video and some audio are inherently non-linguistic. Speech as a spoken language lies in between and often provides valuable information about the subjects, topics and concepts of multimedia content (Lee & Chen, 2005). The language nature of speech makes information extraction from speech less complicated yet more precise and accurate than from image and video. This fact motivates content based speech analysis for multimedia data mining and retrieval where audio and speech processing is a key, enabling technology (Ohtsuki, Bessho, Matsuo, Matsunaga, & Kayashi, 2006). Progress in this area can impact numerous business and government applications (Gilbert, Moore, & Zweig, 2005). Examples are discovering patterns and generating alarms for intelligence organizations as well as for call centers, analyzing customer preferences, and searching through vast audio warehouses.

Download Full-text

Visual Data Mining for Discovering Association Rules

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch125 ◽

2008 ◽

pp. 2105-2120

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

Download Full-text