Owing to rapid developments in digital technologies, the use of electronic media to capture, process and accumulate the information is witnessing an extraordinary development [1]. The stored information is reaching zeta-bytes [2], whereas our capability to scrutinize such large amount of data lags far behind the growth. One of the impediments is the high dimensionality of the datasets. This includes information in different application areas, such as in electronic health records (EHRs), biology, astronomy, medical imaging, video archiving, and web data. Different data mining techniques have been used to extract knowledge available in some of these data sets, albeit with limited success [3].
A number of data mining techniques and models
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For any given point in a high dimensional data space, the expected gap between Euclidean distance to the closest neighbor and to the farthest point in the space is minimized as the dimensionality grows [5]. Therefore, the notion of nearest neighbor is meaningless. This may lead many data mining tasks, like clustering, to become largely ineffective, as the model becomes more and more susceptible to noise present within the data.
To handle the problem of high dimensionality of such data sets, a number of algorithms have been introduced, which use row-based enumeration techniques [6, 7] instead of using column-based enumeration algorithms such as [8-10]. These methods work based on the assumption that the data sets have thousands of columns or have a lot of dimensions, but smaller number of rows. Carpenter algorithm [6] uses a bottom-up method while the TD-close Algorithm [7] uses a top-down exploration approach. Both of these techniques however, work best for dense datasets. Also, these algorithms work only for high-dimensional datasets that have significantly lesser number of rows compared to number of columns. Different dimension reduction algorithms, such as Principal Component Analysis [11], Multi-Dimensional scaling [12] and Independent component analysis [13] are effective and popular algorithms for reducing dimensionality. However, the effectiveness of these different algorithms is limited due to their global linearity.
Multimedia data mining has been shown in fig.1 is a subfield of data mining that used to find interesting information of implicit knowledge. Multimedia data are classified into five types, there are (i) text data (ii) image data (iii) audio data (iv) video data and (v) electronic and digital ink [2]. Text data can be used in web browsers, messages like MMS and SMS. Image data can be used in art work and pictures with text still images taken by a digital camera. Audio data contains sound, MP3 songs, speech and music. Video data include time aligned sequence of frames, MPEG videos from desktops, cell phones, video cameras. Electronic and digital ink
Data mining has been come increasingly easier in recent years. It cannot be done manually because it requires applying mathematics, statistics, and pattern matching to large amounts of data[iv] but advances in computer hardware and software have made data mining on a large scale a reality. This has
Data mining is the extraction of knowledge from the various databases that was previously unknown (Musan & Hunyadi, 2010). Data mining consists of using software that conglomerates artificial intelligence, statistical analysis, and systems management in the act of extracting facts and understanding from data stored in data warehouses, data marts, and through metadata (Giudici, 2005). Through algorithms and learning capabilities data mining software can analyze large amounts of data and give the management team intellectual and effective information to help them form their decisions. The intention for data mining is to analyze prevailing data and form new truths and new associations that were unknown prior to the analysis (Musan & Hunyadi,
With rapid advancements in the technology, new concepts are hitting the industry and it is redefining itself over a course of time. The data mining is one of its kind to improvise the lives of people. Data mining uses techniques which are helpful in finding out the different forms of data. The data mining is closely related to the database technology. Almost every industry takes the help of the datamining to grow in their respective fields. For instance, stock management, quality control, risk management, fraud detection, marketing and analysis of investments. It has its applications ranging from finding the molecule structure of the gene to identifying a robbery at an international level.
In today’s database landscape, much of an organization’s raw data is known to be housed in heterogeneous data stores. In order to merge this siloed data, it must be extracted, transformed, and loaded (ETL) into a common platform known as a Data Warehouse. Once in the Data Warehouse, the data can thus be analyzed using multidimensional data mining (OLAP).
multiple datasets. Implementation details are given in Sect. 5, and the performance is evaluated in Sect. 6 and compared to the state of the art. Our experiments on two challenging datasets (UCF- 101 [24] and HMDB-51 [16])
Some important clustering algorithms discussed in this paper to group massive data and can be useful to industries and organization:
Data mining is a new technology which could be used in extracting valuable information from data warehouses and databases of companies and governments. It involves the extraction of hidden information from some raw data. It helps in detecting inconsistency in data and predicting future patterns and attitude in a highly proficient way. Data mining is implemented using various algorithm and framework, and the automated analysis provided by this algorithm and framework go ahead of evaluation in dataset to providing solid evidences that human experts would not have been able to detect due to the fact that they
Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases
With 3.2 billion internet users [1] and 6.4 billion internet connected devices by 2016 [2], unprecedented amount of data is being generated and process daily and increasing every year. The advent of web 2.0 has fueled the growth and creation of new and more complex types of data which creates a natural demand to analyze new data sources in order to gain knowledge. This new data volume and complexity of the data is being called Big Data, famously characterised by Volume, Variety and Velocity; has created data management and processing challenges due to technological limitations, efficiency or cost to store and process in a timely fashion. The large volume and complex data is unable to be handled and/or processed by most current information systems in a timely manner and the traditional data mining and analytics methods developed for a centralized data systems may not be practical for big data.
With 3.2 billion internet users [1] and 6.4 billion internet connected devices by 2016 [2], unprecedented amount of data is being generated and process daily and increasing every year. The advent of web 2.0 has fueled the growth and creation of new and more complex types of data which creates a natural demand to analyze new data sources in order to gain knowledge. This new data volume and complexity of the data is being called Big Data, famously characterised by Volume, Variety and Velocity; has created data management and processing challenges due to technological limitations, efficiency or cost to store and process in a timely fashion. The large volume and complex data is unable to be handled and/or processed by most current information systems in a timely manner and the traditional data mining and analytics methods developed for a centralized data systems may not be practical for big data.
In 2013 the overall created and copied data volume in the world was 4.4 ZB and it is doubling in size every two years and, by 2020 the digital universe – the data we create and copy annually – will reach 44 ZB, or 44 trillion gigabytes [1]. Under the massive increase of global digital data, Big Data term is mainly used to describe large-scale datasets. Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making [2]. Volume of Big Data represents the magnitude of data while variety refers to the heterogeneity of the data. Computational advances create a chance to use various types of structured, semi-structured, and
data is also Growing. It has resulted large amount of data stock in databases , depot and other repositories . therefore the Data mining comes into model to explore and analyses the databases to extract the interesting and previously obscure patterns and rules well-known as association rule mining
In data mining, inductive learning techniques are used when constructing a model which ensures that trained data model can be applied for future cases.
The term DM was conceptualised as early as 1990s as a means of addressing the problem of analysing the vast repositories of data that are available to mankind, and being added to continuously. DM has been the oldest yet one of the interesting buzzwords. It involves defining associations, or patterns, or frequent item sets, through the analysis of a given data set. Further-more, the discovered knowledge should be valid, novel, useful, and understandable to the user. Many organizations often underutilize their already existing databases not knowing that there is slot of hidden information that requires to be discovered i.e. interesting patterns or knowledge from these databases. DM disciplines revolve around statistics, artificial intelligence, and pattern recognition. There are two main techniques in DM that is reporting and DM techniques. Our study focuses on semi-automatic DM technique for discovering meaningful relationships from a given data set. There is no hypothesis required to mine the data (Jans 09). The technique uses exploratory analysis with no predetermined notions about what will constitute an ―interesting outcome (Kantardzi 02).