Data Mining Seminar Report

Data mining is the process of extracting valuable insights and knowledge from large volumes of data. It involves applying various techniques and algorithms to identify patterns, relationships, and trends that can be used to make informed business decisions, improve processes, and gain a competitive edge. Data mining encompasses many tasks, including preprocessing, exploratory data analysis, pattern discovery, and predictive modelling.

This abstract provides an overview of the data mining process, highlighting its significance, essential techniques, and real-world applications. It emphasizes the importance of data quality, appropriate data selection, and the need for ethical considerations in data mining. Furthermore, it discusses the challenges and future directions of data mining, including privacy concerns, scalability issues, and the integration of artificial intelligence and machine learning techniques. Overall, data mining offers immense potential for organizations to unlock valuable insights from their data, enabling data-driven decision-making and fostering innovation in various domains.

Related Article: Data Analytics

Data Mining (image is for representation purposes only)

Data Mining Techniques, An Introduction to Data Mining

Data mining is the process of extracting patterns from data. Data mining is becoming increasingly essential to transforming this data into information. It is commonly used in many profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.

Data mining can uncover patterns in data but is often carried out only on data samples. The mining process will be ineffective if the samples are not a good representation of the larger body of data. Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being “mined”. The inability to find ways may cause disputes between customers and service providers. Therefore data mining is not foolproof but may be helpful if sufficiently representative data samples are collected. The discovery of a particular pattern in a particular data set does not necessarily mean that a pattern is found elsewhere in the more critical data from that sample. An essential part of the process is the verification and validation of patterns on other models of data.

The related terms data dredging, data fishing, and data snooping refer to the use of data mining techniques to sample sizes that are (or maybe) too small for statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). Data dredging may, however, be used to develop new hypotheses, which must then be validated with sufficiently large sample sets.

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Data Mining an Overview

Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cut costs, or both. Data mining software is one of several analytical tools for analyzing data. It allows users to analyze data from many dimensions or angles, categorize it, and summarize the identified relationships. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.

Continuous Innovation

Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software dramatically increase analysis accuracy while reducing cost.

Realtime examples of Data Mining technology

For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically grocery shop on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information to increase revenue. For example, they could move the beer display closer to the diaper display. And they could ensure beer and diapers were sold at total price on Thursdays.

Data, Information, and Knowledge
Data is any facts, numbers, or text a computer can process. Today, organizations are accumulating vast and growing amounts of data in different formats and databases. This includes:
operational or transactional data such as sales, cost, inventory, payroll, and accounting
nonoperational data, such as industry sales, forecast data, and macroeconomic data
metadata – data about the information itself, such as logical database design or data dictionary definitions

The patterns, associations, or relationships among all this data can provide information. For example, retail point-of-sale transaction data analysis can yield information on which products are selling and when.

Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to understand consumer buying behaviour. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.

Data Warehouses
Dramatic advances in data capture, processing power, data transmission, and storage capabilities enable organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term, although the concept has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And equally dramatic advances in data analysis software allow users to access this data freely. The data analysis software is what supports data mining.

What can data mining do?

Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills and “external” factors such as economic indicators, competition, and customer demographics. And it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detailed transactional data.

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comments or warranty cards, retailers could develop products and promotions to appeal to specific customer segments.

For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on an analysis of their monthly expenditures.

Walmart is pioneering massive data mining to transform its supplier relationships. Walmart captures point-of-sale transactions from over 2,900 stores in 6 countries and transmits this data to its massive 7.5 terabyte Teradata data warehouse. Walmart allows more than 3,500 suppliers to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.

The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes players’ movements to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995, reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.

Using the NBA universal clock, a coach can automatically bring up the video clips showing each jump shot attempted by Williams with Price on the floor without combing hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knicks ‘ defence and finds Williams for an open jump shot.

How does data mining work?

While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:

Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.

Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.

Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.

Sequential patterns: Data is mined to anticipate behaviour patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a consumer purchasing a backpack based on their purchase of sleeping bags and hiking shoes.

Data mining consists of five major elements

  • 1. Extract, transform, and load transaction data onto the warehouse system.
  • 2. Store and manage the data in a multidimensional database system.
  • 3. Provide data access to business analysts and information technology professionals.
  • 4. Analyze the data using application software.
  • 5. Present the data correctly, such as a graph or table.

Different levels of analysis are available:
Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.

Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi-Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used to classify a dataset. They provide a set of rules you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits, while CHAID segments use chi-square tests to create multi-way splits. CART typically requires less data preparation than CHAID.

Nearest neighbour method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). They are sometimes called the k-nearest neighbour technique.

Rule induction: Extracting applicable if-then rules from data based on statistical significance.

Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.

What technological infrastructure is required?
Today, data mining applications are available on all-size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for most miniature applications to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCR can deliver applications exceeding 100 terabytes. There are two critical technological drivers:

Size of the database: The more data is processed and maintained, the more influential the system is.

Query complexity: the more complex the queries and the greater the number of questions being processed, the more influential the system requires.

Relational database storage and management technology are adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.

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Related: Artificial Intelligence Seminar Topics prepared and published this seminar report on Data Mining for Engineering degree students’ seminar topic preparation. Before shortlisting your topic, you should do your research in addition to this information. Please include Reference: and link back to Collegelib in your work.

This article was initially published on Collegelib in 2012.