Data Mining: Importance, Process, and Applications
What Motivated Data Mining? Why Is It Important?
The major reason that data mining has attracted a great deal of attention in the information industry in recent years is due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from business management, production control, and market analysis, to engineering design and science exploration.
Evolution of Database Technology
What Is Data Mining?
Data mining refers to extracting or mining knowledge from large amounts of data. There are many other terms related to data mining, such as knowledge mining, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. Many people treat data mining as a synonym for another popularly used term, Knowledge Discovery in Databases, or KDD.
Essential Step in the Process of Knowledge Discovery in Databases
Knowledge discovery as a process is depicted in the following figure and consists of an iterative sequence of the following steps:
- data cleaning: to remove noise or irrelevant data
- data integration: where multiple data sources may be combined
- data selection: where data relevant to the analysis task are retrieved from the database
- data transformation: where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations
- data mining: an essential process where intelligent methods are applied in order to extract data patterns
- pattern evaluation to identify the truly interesting patterns representing knowledge based on some interestingness measures
- knowledge presentation: where visualization and knowledge representation techniques are used to present the mined knowledge to the user.
Architecture of a Typical Data Mining System/Major Components
Data mining is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. Based on this view, the architecture of a typical data mining system may have the following major components:
- A database, data warehouse, or other information repository, which consists of the set of databases, data warehouses, spreadsheets, or other kinds of information repositories containing the student and course information.
- A database or data warehouse server which fetches the relevant data based on users’ data mining requests.
- A knowledge base that contains the domain knowledge used to guide the search or to evaluate the interestingness of resulting patterns. For example, the knowledge base may contain metadata which describes data from multiple heterogeneous sources.
- A data mining engine, which consists of a set of functional modules for tasks such as classification, association, classification, cluster analysis, and evolution and deviation analysis.
- A pattern evaluation module that works in tandem with the data mining modules by employing interestingness measures to help focus the search towards interesting patterns.
- A graphical user interface that allows the user an interactive approach to the data mining system.
How Is a Data Warehouse Different from a Database? How Are They Similar?
• Differences between a data warehouse and a database: A data warehouse is a repository of information collected from multiple sources, over a history of time, stored under a unified schema, and used for data analysis and decision support; whereas a database is a collection of interrelated data that represents the current status of the stored data. There could be multiple heterogeneous databases where the schema of one database may not agree with the schema of another. A database system supports ad-hoc query and on-line transaction processing. For more details, please refer to the section ‘Differences between operational database systems and data warehouses.’
• Similarities between a data warehouse and a database: Both are repositories of information, storing huge amounts of persistent data.
Data Mining: On What Kind of Data? / Describe the Following Advanced Database Systems and Applications
In principle, data mining should be applicable to any kind of information repository. This includes relational databases, data warehouses, transactional databases, advanced database systems, flat files, and the World-Wide Web. Advanced database systems include object-oriented and object-relational databases, and special application-oriented databases, such as spatial databases, time-series databases, text databases, and multimedia databases.
Flat Files:
Flat files are actually the most common data source for data mining algorithms, especially at the research level. Flat files are simple data files in text or binary format with a structure known by the data mining algorithm to be applied. The data in these files can be transactions, time-series data, scientific measurements, etc.
Relational Databases:
A relational database consists of a set of tables containing either values of entity attributes, or values of attributes from entity relationships. Tables have columns and rows, where columns represent attributes and rows represent tuples. A tuple in a relational table corresponds to either an object or a relationship between objects and is identified by a set of attribute values representing a unique key. In the following figure, it presents some relations Customer, Items, and Borrow representing business activity in a video store. These relations are just a subset of what could be a database for the video store and is given as an example.
The most commonly used query language for relational database is SQL, which allows retrieval and manipulation of the data stored in the tables, as well as the calculation of aggregate functions such as average, sum, min, max, and count. For instance, an SQL query to select the videos grouped by category would be:
SELECT count(*) FROM Items WHERE type=video GROUP BY category.
Data mining algorithms using relational databases can be more versatile than data mining algorithms specifically written for flat files, since they can take advantage of the structure inherent to relational databases. While data mining can benefit from SQL for data selection, transformation, and consolidation, it goes beyond what SQL could provide, such as predicting, comparing, detecting deviations, etc.
Data Warehouses
A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and which usually resides at a single site. Data warehouses are constructed via a process of data cleansing, data transformation, data integration, data loading, and periodic data refreshing. The figure shows the basic architecture of a data warehouse
In order to facilitate decision making, the data in a data warehouse are organized around major subjects, such as customer, item, supplier, and activity. The data are stored to provide information from a historical perspective and are typically summarized.
A data warehouse is usually modeled by a multidimensional database structure, where each dimension corresponds to an attribute or a set of attributes in the schema, and each cell stores the value of some aggregate measure, such as count or sales amount. The actual physical structure of a data warehouse may be a relational data store or a multidimensional data cube. It provides a multidimensional view of data and allows the precomputation and fast accessing of summarized data.
Data Mining Functionalities/Data Mining Tasks: What Kinds of Patterns Can Be Mined?
Data mining functionalities are used to specify the kind of patterns to be found in data mining tasks. In general, data mining tasks can be classified into two categories:
- Descriptive
- predictive
Describe data mining functionalities, and the kinds of patterns they can discover
(or)
Define each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classification, prediction, clustering, and evolution analysis. Give examples of each data mining functionality, using a real-life database that you are familiar with.
Classification:
Classification:
- It predicts categorical class labels
- It classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data
- Typical Applications
- credit approval
- target marketing
- medical diagnosis
- treatment effectiveness analysis
Classification can be defined as the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (i.e., data objects whose class label is known).
Example:
An airport security screening station is used to determine if passengers are potential terrorists or criminals. To do this, the face of each passenger is scanned and its basic pattern (distance between eyes, size, and shape of mouth, head, etc.) is identified. This pattern is compared to entries in a database to see if it matches any patterns that are associated with known offenders
A classification model can be represented in various forms, such as
1) IF-THEN rules,
student (class, ‘undergraduate’) AND concentration (level, ‘high’) ==> class A
student (class, ‘undergraduate’) AND concentration (level, ‘low’) ==> class B
student (class, ‘post graduate’) ==> class C
2) Decision tree
Clustering Analysis
Clustering analyzes data objects without consulting a known class label.
The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity.
Each cluster that is formed can be viewed as a class of objects.
Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together as shown below:
Example:
A certain national department store chain creates special catalogs targeted to various demographic groups based on attributes such as income, location, and physical characteristics of potential customers (age, height, weight, etc). To determine the target mailings of the various catalogs and to assist in the creation of new, more specific catalogs, the company performs a clustering of potential customers based on the determined attribute values. The results of the clustering exercise are then used by management to create special catalogs and distribute them to the correct target population based on the cluster for that catalog.
Classification vs. Clustering
- In general, in classification you have a set of predefined classes and want to know which class a new object belongs to.
- Clustering tries to group a set of objects and find whether there is some relationship between the objects.
- In the context of machine learning, classification is supervised learning and clustering is unsupervised learning.
Outlier Analysis: A database may contain data objects that do not comply with the general model of data. These data objects are outliers. In other words, the data objects which do not fall within the cluster will be called as outlier data objects. Noisy data or exceptional data are also called as outlier data. The analysis of outlier data is referred to as outlier mining.
Example
Outlier analysis may uncover fraudulent usage of credit cards by detecting purchases of extremely large amounts for a given account number in comparison to regular charges incurred by the same account. Outlier values may also be detected with respect to the location and type of purchase, or the purchase frequency.
Correlation Analysis
Correlation analysis is a technique used to measure the association between two variables. A correlation coefficient (r) is a statistic used for measuring the strength of a supposed linear association between two variables. Correlations range from -1.0 to +1.0 in value.
A correlation coefficient of 1.0 indicates a perfect positive relationship in which high values of one variable are related perfectly to high values in the other variable, and conversely, low values on one variable are perfectly related to low values on the other variable.
A correlation coefficient of 0.0 indicates no relationship between the two variables. That is, one cannot use the scores on one variable to tell anything about the scores on the second variable.
A correlation coefficient of -1.0 indicates a perfect negative relationship in which high values of one variable are related perfectly to low values in the other variables, and conversely, low values in one variable are perfectly related to high values on the other variable.
Data Preprocessing
Data preprocessing describes any type of processing performed on raw data to prepare it for another processing procedure. Commonly used as a preliminary data mining practice, data preprocessing transforms the data into a format that will be more easily and effectively processed for the purpose of the user.
Why Data Preprocessing?
Data in the real world is dirty. It can be incomplete, noisy, and inconsistent from. These data need to be preprocessed to help improve the quality of the data and quality of the mining results.
- If no quality data, then no quality mining results. The quality decision is always based on the quality data.
- If there is much irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase is more difficult
Incomplete data: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data. e.g., occupation=’ ‘.
Noisy data: containing errors or outliers data. e.g., Salary=’-10′
Inconsistent data: containing discrepancies in codes or names. e.g., Age=’42’ Birthday=’03/07/1997′
- Incomplete data may come from
- ‘Not applicable’ data value when collected
- Different considerations between the time when the data was collected and when it is analyzed.
- Human/hardware/software problems
- Noisy data (incorrect values) may come from
- Faulty data collection by instruments
- Human or computer error at data entry
- Errors in data transmission
- Inconsistent data may come from
- Different data sources
- Functional dependency violation (e.g., modify some linked data)
Major Tasks in Data Preprocessing
- Data cleaning
- Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies
- Data integration
- Integration of multiple databases, data cubes, or files
- Data transformation
- Normalization and aggregation
- Data reduction
- Obtains reduced representation in volume but produces the same or similar analytical results
- Data discretization
- Part of data reduction but with particular importance, especially for numerical data
Forms of Data Preprocessing
Data Cleaning:
Data cleaning routines attempt to fill in missing values, smooth out noise while identifying outliers, and correct inconsistencies in the data.
Various methods for handling this problem:
The various methods for handling the problem of missing values in data tuples include:
(a) Ignoring the tuple: This is usually done when the class label is missing (assuming the mining task involves classification or description). This method is not very effective unless the tuple contains several
attributes with missing values. It is especially poor when the percentage of missing values per attribute
varies considerably.
(b) Manually filling in the missing value: In general, this approach is time-consuming and may not be a reasonable task for large data sets with many missing values, especially when the value to be filled in is not easily determined.
(c) Using a global constant to fill in the missing value: Replace all missing attribute values by the same constant, such as a label like ‘Unknown,’ or -∞. If missing values are replaced by, say, ‘Unknown,’ then the mining program may mistakenly think that they form an interesting concept, since they all have a value in common – that of ‘Unknown.’ Hence, although this method is simple, it is not recommended.
(d) Using the attribute mean for quantitative (numeric) values or attribute mode for categorical (nominal) values, for all samples belonging to the same class as the given tuple: For example, if classifying customers according to credit risk, replace the missing value with the average income value for customers in the same credit risk category as that of the given tuple.
(e) Using the most probable value to fill in the missing value: This may be determined with regression, inference-based tools using Bayesian formalism, or decision tree induction. For example, using the other customer attributes in your data set, you may construct a decision tree to predict the missing values for income.
Noisy Data:
Noise is a random error or variance in a measured variable.
Clustering: Outliers in the data may be detected by clustering, where similar values are organized into groups, or ‘clusters’. Values that fall outside of the set of clusters may be considered outliers.
