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Information Technology: Data Mining

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1.1. DATA MINING Data mining refers to extracting or mining knowledge from large amounts of data. Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the forthcoming need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and science exploration. Data mining can be viewed as a result of the natural evolution of information technology. The database system industry has witnessed an evolutionary path in the development of the following functionalities …show more content…

IMAGE MINING Image Mining is an extended branch of data mining that is concerned with the process of knowledge discovery from images. Image mining deals with the extraction of image patterns from a large collection of images. It can be done manually by slicing and dicing the data until a pattern becomes obvious. Or, it can be done with programs that analyze the data automatically. Color, texture and shape of an image have been primitive image descriptors in Content Based Image Retrieval (CBIR) system. Primitive features of an image are used to identify and retrieve closely matched images from an image database. It is very difficult to extract images manually from image database because they are very large. Mining useful content from image collections is very much an interdisciplinary endeavor that draws upon expertise in computer vision, image understanding, data mining, machine learning, databases, distributed/parallel computing, software design, and artificial …show more content…

Image mining draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Image mining is focused on extracting patterns, implicit knowledge, image data relationship or patterns which are not explicitly found in the images from databases or collections of images. Some of the methods used to gather knowledge are: image retrieval, data mining, image processing and artificial intelligence. These methods allow image mining to have two different approaches. First, is to extract only from databases or collections of images, and second, dig or mine a combination of associated alphanumeric data and collections of images. Image miners are capable of using existing techniques in order to mine for knowledge. Some of these techniques

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After, the color space transformation we are going to extracts the texture vector from that image using sparse texture model. The texture vectors are represented as a set of distributions which is used to cluster the texture data using K-means clustering algorithm. Finding the number of clusters which consists set of texture distributions used to calculate TD metric. After, calculating the TD metric, the image is over segmented using SRM algorithm, which results the image being divided into large number of regions. Next, each region is independently classified as representing normal skin or lesion based on the textural contents of that region.

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In developing a database, one of the first things one must know is how the database(DB) will be used within the organization. Seconda,y what type of data will be required to develop the database and how it will enhance productivity and reliability to the organization. All the information is gathered in the first phase of the database life cycle, which is planning. In the planning phase, you are gathering information on the need, cost and feasibility of the database within the organization. Also within this phase you would look to see if there are databases within the organization that can meet the requirements.

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1) Read all the input images and load the images into the database. I =imread(name of the image) imshow(I) set the position of image set(gcf,"position",[1,1,500,500]) Only the position and directions of these features are stored. 2) Enhancement of the features The enhancement of the image depends upon the quality of the input image to ensure the identification and verification system.

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This is done by calculating the hash value of the image, both before and after the image is

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The financial managers will be able to pick the purchasing trends at the right time which will enable them to manage the cash flows strategies efficiently enhancing overall financial position of the Best Buy Co., Inc. 3.3. Proposed BI Solution Data Mining Coupled with Digital Dashboard – The proposed BI solution will enable the Best Buy Co., Inc. to use its current data infrastructure and combine the data from various sources at centralized system from where the customized reports can be generated based on the specific requirements of the user. In this regard the bottom up approach would most appropriate because all the data is already being collected at various levels and the company needs federated repository. Following are the key feature of the proposed BI solution: • Automated data preparation across a variety of data sources. • Advanced statistical and machine-learning

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It is the specialty of sending and getting scrambled messages that can be decoded just by the sender or the collector. Encryption and using so as to decode are proficient scientific calculations in such a way that nobody however the proposed beneficiary can unscramble and read the message. Naor and Shamir presented the visual cryptography plan (VCS) as a straightforward and secure approach to permit the mystery sharing of pictures with no cryptographic calculations. (Divya James, & Mintu Philip 2012).

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Data mining is one of the computing processes that support discovering the large data sets which involves methods. The main task of data mining is to come across unexpected, useful and interesting pattern in a large database (Neha Goyal et al., 2016). Pattern mining is a sub domain of data mining which detects the stable frequent patterns between data. Frequent patterns are nothing but a substructures or sequences which are defined by the user on transactional database that is equal or greater than a threshold. This pattern mining can be applied on various types of data such as transaction database, stream database and sequence database.

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Big Data Big data is a common business buzzword that is usually used in reference to concepts such as consumer data or marketing demographics. However, big data analytics has excellent applications for health care organization. Big data will allow health care companies to effectively analyze and understand health care trends and costs. It will also result in increased administration efficiency, patient satisfaction and operational processes. However, big data can only be understood through historic and current records.

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My scores from the LCI are as following: Sequence - 28 , Precision - 27, Technical - 24, and Confluence - 24. From my scores it shows I'm a Dynamic Learner. I use at least two Patterns as Use First Levels, then I use the remainders as either Use as Needed or Avoid Pattern. In my case I use them as Use as Needed Patterns. As a Dynamic Learner can move from one Pattern to another within one setting.

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In today 's society the most commonly know thing many Americans enjoy doing is going out to eat, going to the movies and working out. There are so many different types of way a person can exercise, usually people prefer going to the gym and work out. Many people also enjoy outdoor sport like running, playing sports, group exercise, weight lifting as their form of exercise. Most of the people who attend a gym, go to lift weight, in fact some of those people commit to losing weight instead of lift weight for muscle. Those that enjoy lifting weights often enjoy working out in the privacy of their home, at community centers like YMCA, at a health club or a gym.

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The firm procures raw materials and components across the world and continually examines its production requirement against its manufacturing capacities to pursue cost reduction. Capabilities  Thanks to the cloud, Revlon has been able to resolve the difficulties of big-data management efficiently by classifying all the unstructured data in the company (Swan,

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Information processing theory The information processing theory is a structure which rationalises how people obtain; process and store information and knowledge (Tangen & Borders 2017, p. 99). The Information processing theory involves the clinical reasoning cycle and the information processing model. The clinical reasoning cycle is a model which guides nurses and other health practitioners in making clinical judgements (Levett-Jones 2018, p. 4).

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Even though organizations hold huge amount of data, they cannot use them effectively as they are unstructured. However new technologies are now available which enable analysis of large, complex, unstructured data. The accessibility of technology has become easy; as a result, there is massive increase in data amounts available with the entrepreneurs. The data usage depends on the ability the way it is stored, managed and then analyzing it adequately. Big data is an upcoming and emerging trend in the field of Information technology.

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As big data things continue to grow in this modern era, today we can learn how to predict or assume anything that will happen in the future with data from the past. This studies known as Predictive Analytics. Predictive analytics combine methods from machine learning, data mining and statistics to find meaning or pattern from a huge volume of data. Tom H Davenport, a senior advisor at Deloitte Analytics has broken down three primer models on doing predictive analytics: the data, statistics, and assumptions.

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Big Data There are many different definitions for Big Data. SAS (n.d.) an analytical software company describes it as, “a popular term used to describe the exponential growth and availability of data, both structured and unstructured.” Many think Big Data just came into existence but it has been around for years. Banks, retail, advertisers have been using big data for marketing purposes.

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