Data Mining for Command Line Interface -myassignmenthelp.com

Question: Discuss about theData Mining for Command Line Interface. Answer: Features of Data Mining tool Data mining tools have various features which perform various functions. The key features include graphical interface, command line interface, API, algorithms, In-memory, interactive dashboard, multiple file support, and data management methods. Each data mining tool has an interface which allows users to interact with the tool. Some tools have a graphical user interface (GUI) while others have both GUI and command line interfaces. GUI is aimed at allowing users to complete data mining projects without the need of programming languages (Mikut, 2011). GUI is relatively easy to use for most people especially non-programmers. This is because command line interface (CLI) require technical knowledge in various programming languages such as python, R, Java, etc. Data mining tools with CLI allow users to access all features and are useful for scripting large data mining jobs. Most data mining tools have APIs which are key in data mining (Han, 2011). These APIs are used to perform varying functions. For example, a particular API can be used to mine trends from input data. Webhost.io is a data mining API that allows users to find structured web data that an be leveraged to scale big data operations. Other functions of the APIs include extracting data from the web, grouping sentences or short texts, retrieving data from wiki data store, encrypt data, etc. Data mining tools such as Weka and Rapid Miner have an API which can be integrated into custom applications. Data mining is dependent on algorithms which are designed to analyze specific aspects in a dataset. Data mining tools have a set of algorithms which are used independently or in combination to analyze the data. Selection algorithms are the most common in data mining tools. They are classified as wrapper, filter, and embedded methods. Filter methods rely on a measure to assign a score to each feature. Some of the filter methods include information gain, and Chi-squared test. Wrapper methods view the selection process as a search problem which involves different combinations that have to be compared. The methods consider a predictive model which assigns a score and apply a methodical search process. Recursive feature elimination algorithm is an example of a wrapper method. Also, embedded method is one of the features in data mining tools which are in improving the accuracy of models. It is common for data mining tools to have an in-memory database which is a system that uses main memory for data storage. Main memory is much faster than disk databases as access to disk tends to be slow. This feature is critical in enhancing the processing speed of the tool when mining data. Main memory incorporates simple internal optimization algorithms and has few CPU instructions which enhances the performance of the data mining tool. These tools also integrate an interactive dashboard that includes various options that users can leverage to view the results of the data mining process. The dashboard is aimed at making the tools easy to use for many people (Romero, 2008). With a dashboard, new users can easily apply data filters and algorithms to analyze the datasets available. It also allows users to create charts and graphs from the data. Since data mining tools handle various kinds of data from different sources, they support multiple file formats. Some tools may handle specific data format, but most support a lot of formats. Some of the formats supported including CSV, XML, HTML/A, TIFF, GeoTIFF, MP3, MOV, among others. Multiple file support is an important feature that is considered when purchasing data mining tool. Additionally, these tools have various data management methods aimed at enhancing the data mining process. Some of the methods are data preparation and data filtering. Before processing the dataset obtained, the data has to be filtered to avoid skewed results that may not represent the reality. How data mining realize the value of data warehouse Data warehouse plays an instrumental role in integrating data from different databases. The objective of the data warehouse is not to store data but help firms to make informed decisions based on the insight gained from the data. It supports this goal by offering an architecture for organizing and assessing data from various sources. In data warehouses, data may be stored in flat files, database tables, or spreadsheets. To realize the value of a data warehouse, it is critical to obtain knowledge from the information stored. However, due to the amount and complexity of data, it is challenging for data analysts to determine trends and relationships using simple query tools. Data mining is an effective way of extracting knowledge such as trends and patterns from the data. Data mining process involves analyzing data and generating useful information. It relies on complex data analysis tools to identify patterns and relationships in datasets stored within the data warehouse. These tools are more advanced than querying tools as they use complicated algorithms to analyze the datasets (Van der Aalst, 2011). With data mining, firms can leverage their large datasets to identify patterns that may have business implications. Many businesses apply data mining to gain business intelligence that is vital in aligning with market trends and competing with rivals. Data mining enables businesses with warehouses to identify patterns that can be used to predict trends. Data mining tools include predictive models which assist in predicting future trends based on the patterns observed in the datasets. For example, a fashion company that sells fashion products to its customers and has a data warehouse can leverage data mining tools to predict future trends in the fashion industry. Data on customer purchasing behavior as well as customer growth can be vital in predicting business growth expected by the company (Ngai, 2009). For firms that work in the marketing industry, it is essential to understand customer behavior and habits. Such firms have data warehouses that hold customer data and their purchasing history. With data mining systems, the firms can analyze customer data and determine customer profiles. Results from such a process are helpful in monitoring customer habits. The firms can gain value from the results by leveraging them to build customer-oriented marketing campaigns. Knowledge can be vital when making decisions. While data warehouse contains vast amounts of data, firms cannot benefit from it unless they obtain knowledge. Data mining helps to identify important patterns and relationships which can be incorporated into business applications. Through data mining, firm managers can have access to crucial insight that can help them to make precise business decisions. Firms that collect information from their customer and operations have a competitive advantage over their rivals. Such information can be mined to acquire knowledge about market changes, customer, and preferences. Data mining is key in supporting market-based analysis on the data available in the data warehouses. The process involves information that is gathered on the basis of market information from various sources. With data mining tools, firms can analyze the market and identify key trends that should be considered in business planning to maintain the competitiveness of the firm in the market. Additionally, data mining allows banks to gain value and protect their operations. Marketing analysis which is enabled by data mining process helps bank firms to find fraud. Banks can identify customers involved in fraud and close their accounts to protect their operations. References Han, J., Pei, J., Kamber, M. (2011).Data mining: concepts and techniques. Elsevier. Mikut, R., Reischl, M. (2011). Data mining tools.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,1(5), 431-443. Ngai, E. W., Xiu, L., Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification.Expert systems with applications,36(2), 2592-2602. Romero, C., Ventura, S., Garca, E. (2008). Data mining in course management systems: Moodle case study and tutorial.Computers Education,51(1), 368-384. Van der Aalst, W. M. (2011). Data Mining. InProcess Mining(pp. 59-91). Springer Berlin Heidelberg.