Sunday, 27 January 2013

Data mining unit-1 chapter-1


DATA WAREHOUSING & DATA MINING

Fundamentals of Data mining:

What Motivated Data Mining? Why Is It Important?

            Data mining is due to the wide availability of huge amounts of data and the approach 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 in the development of the following functionalities: data collection and database creation, data management (including data storage and retrieval, and database transaction processing), and advanced data analysis (involving data warehousing and data mining).

Evolution of Database Technology
n  1960s:
n  Data collection, database creation, IMS and network DBMS
            Since the 1960s, database and information technology has been evolving systematically from primitive file processing systems to sophisticated and powerful database systems. The research and development in database systems
n  1970s:
n  Relational data model, relational DBMS implementation
            since the 1970s has progressed from early hierarchical and network database systems to the development of relational database systems (where data are stored in relational table structures; data modeling tools, and indexing and accessing methods.
Efficient methods for on-line transaction processing (OLTP).
n  1980s:
n  RDBMS, advanced data models (extended-relational, OO, deductive, etc.)
                                    Research and development activities on new and powerful database systems
n  Application-oriented DBMS (spatial, scientific, engineering, etc.)
                                    Application-oriented database systems, including spatial, temporal,                                            multimedia, active, stream, and sensor, and scientific and engineering databases,
Knowledge bases, and office information bases, have flourished
n  1990s:
n  Data mining, data warehousing, multimedia databases, and Web databases
n  2000s
n  Stream data management and mining
n  Data mining and its applications
n  Web technology (XML, data integration) and global information systems.


What Is Data Mining?

n  Data mining (knowledge discovery from data)
n  Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data.
n  The term is actually a misnomer. A misnomer that carries both “data” and “mining”
                        became a popular choice. Many other terms carry a similar or slightly different meaning                           to data mining, such as knowledge mining from data, knowledge extraction, data/pattern                             analysis, data archaeology, and data dredging.

Knowledge Discovery (KDD) Process
            Many people treat data mining as a synonymfor another popularly used term, Knowledge Discovery from Data, or KDD. Alternatively, others view data mining as simply an essential step in the process of knowledge discovery. Knowledge discovery as a process  and consists of an iterative sequence of the following steps:
1. Data cleaning (to remove noise and inconsistent data)
2. Data integration (where multiple data sources may be combined)1
3. Data selection (where data relevant to the analysis task are retrieved fromthe database)
4. Data transformation (where data are transformed or consolidated into forms appropriate
    for mining by performing summary or aggregation operations, for instance)2
5. Data mining (an essential process where intelligent methods are applied in order to
extract data patterns)
6. Pattern evaluation (to identify the truly interesting patterns representing knowledge
based on some interestingness measures; Section 1.5)
7. Knowledge presentation (where visualization and knowledge representation techniques
are used to present the mined knowledge to the user)

 Data Mining—On What Kind of Data?
            Data mining should be applicable to any kind of data repository, as well as to transient data, such as data streams. Thus the scope of our examination of data repositories will include relational databases, data warehouses, transactional databases, advanced database systems, flat files, data streams, and the
World Wide Web. Advanced database systems include object-relational databases and specific   application-oriented databases, such as spatial databases, time-series databases, text databases, and multimedia databases.
1.3.1 Relational Databases
A database system, also called a database management system (DBMS), consists of a collection of interrelated data, known as a database, and a set of software programs to manage and access the data.
            A Relational database is a collection of tables, each of which is assigned a unique name. Each table consists of a set of attributes (columns or fields) and usually stores a large set of tuples (records or rows). Each tuple in a relational table represents an object identified by a unique key and described by a set of attribute values. A semantic data model, such as an entity-relationship (ER) data model, is often constructed for relational databases.
1.3.2 Data Warehouses
            A data warehouse is a repository of information collected from multiple sources, stored under a unified schema, and that usually resides at a single site. Data warehouses are constructed via a process of data cleaning, data integration, data transformation, data loading, and periodic data refreshing.
Figure  shows the typical framework for construction and use of a data warehouse
for AllElectronics.


1.3.3 Transactional Databases
In general, a transactional database consists of a file where each record represents a transaction.
A transaction typically includes a unique transaction identity number (trans ID) and a list of the items making up the transaction (such as items purchased in a store). The transactional database may have additional tables associated with it, which contain other information regarding the sale, such as the date of the transaction, the customer ID number and so on.
1.3.4 Advanced Data and Information Systems and Advanced Applications
            , various kinds of advanced data and information systems have emerged and are undergoing development to address the requirements of new applications.
            The new database applications include handling spatial data (such as maps), engineering design data (such as the design of buildings, system components, or integrated circuits), hypertext and multimedia data (including text, image, video, and audio Data), time-related data (such as historical records or stock exchange data), stream data (such as video surveillance and sensor data, where data flow in and out like streams), and the WorldWideWeb (a huge, widely distributed information repository made available by the Internet).
1.3.5 Object-Relational Databases
Object-relational databases are constructed based on an object-relational data model. This model extends the relational model by providing a rich data type for handling complex objects and object orientation. Conceptually, the object-relational data model inherits the essential concepts of
object-oriented databases, where, in general terms, each entity is considered as an object.
Each object has associated with it the following:
A set of variables that describe the objects. These correspond to attributes in the entity-relationship and relational models.
A set of messages that the object can use to communicate with other objects, or with the rest of the database system.
A set of methods, where each method holds the code to implement a message. Upon receiving a message, the method returns a value in response.
1.3.6 Temporal Databases, Sequence Databases, and
Time-Series Databases
            A temporal database typically stores relational data that include time-related attributes.
These attributes may involve several timestamps, each having different semantics.
            A sequence database stores sequences of ordered events, with or without a concrete
notion of time.
            A time-series database stores sequences of values or events obtained over repeated measurements of time (e.g., hourly, daily, weekly).

1.3.7 Spatial Databases and Spatiotemporal Databases
            Spatial databases contain spatial-related information. Examples include geographic (map) databases, very large-scale integration (VLSI) or computed-aided design databases, and medical and satellite image databases. Spatial data may be represented in raster format, consisting of n-dimensional bit maps or pixel maps.
            A spatial database that stores spatial objects that change with time is called a spatiotemporal database, from which interesting information can be mined. For example, we may be able to group the trends of moving objects and identify some strangely moving vehicles.

1.3.8 Text Databases and Multimedia Databases
            Text databases are databases that contain word descriptions for objects. These word descriptions are usually not simple keywords but rather long sentences or paragraphs, such as product specifications, error or bug reports, warning messages, summary reports, notes, or other documents. Text databases may be highly unstructured.
            Multimedia databases store image, audio, and video data. They are used in applications such as picture content-based retrieval, voice-mail systems, video-on-demand systems, the World Wide Web, and speech-based user interfaces that recognize spoken commands.
1.3.9 Heterogeneous Databases and Legacy Databases
            A heterogeneous database consists of a set of interconnected, autonomous component databases. The components communicate in order to exchange information and answer queries.
            A legacy database is a group of heterogeneous databases that combines different kinds of data systems, such as relational or object-oriented databases, hierarchical databases, network databases, spreadsheets, multimedia databases, or file systems. The heterogeneous databases in a legacy database may be connected by intra or inter-computer networks.
1.3.10 Data Streams
Many applications involve the generation and analysis of a new kind of data, called stream data, where data flow in and out of an observation platform (or window) dynamically. Such data streams have the following unique features: huge or possibly infinite volume, dynamically changing, flowing in and out in a fixed order, allowing only one or a small number of scans, and demanding fast (often real-time) response time.
1.3.11 TheWorld WideWeb
The World Wide Web and its associated distributed information services, such as
Yahoo!, Google, America Online, and AltaVista, provide rich, worldwide, on-line information
services, where data objects are linked together to facilitate interactive access.


1.3 Data Mining Functionalities—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 and predictive. Descriptive mining tasks characterize the general properties of the data in the database. Predictive mining tasks perform inference on the current data in order to make predictions.
            Data mining systems should also allow users to specify hints to guide or focus the search for interesting patterns. Because some patterns may not hold for all of the data in the database, a measure of certainty or “trustworthiness” is usually associated with each discovered pattern. Data mining functionalities, and the kinds of patterns they can discover, are described below.
1.3.1 Concept/Class Description: Characterization and Discrimination
            Data can be associated with classes or concepts. Descriptions of a class or a concept are called class/concept descriptions. These descriptions can be derived via
(1) data characterization, by summarizing the data of the class under study (often called the target class) in general terms, or
 (2) data discrimination, by comparison of the target class with one or a set of comparative classes (often called the contrasting classes), or
(3) both data characterization and discrimination. Data characterization is a summarization of the general characteristics or features of a target class of data. The data corresponding to the user-specified class are typically collected by a database query.
            An attribute-oriented induction technique can be used to perform data generalization and
characterization without step-by-step user interaction.
            The output of data characterization can be presented in various forms. Examples include pie charts, bar charts, curves, multidimensional data cubes, and multidimensional tables, including crosstabs. The resulting descriptions can also be presented as generalized relations or in rule form(called characteristic rules).
            Data discrimination is a comparison of the general features of target class data objects with the general features of objects from one or a set of contrasting classes. The target  and contrasting classes can be specified by the user, and the corresponding data objects retrieved through database queries. The methods used for data discrimination are similar to those used for data characterization.
1.3.2 Mining Frequent Patterns, Associations, and Correlations
Frequent patterns, as the name suggests, are patterns that occur frequently in data. There are many kinds of frequent patterns, including itemsets, subsequences, and substructures. A substructure can refer
to different structural forms, such as graphs, trees, or lattices, which may be combined with item sets or subsequences. If a substructure occurs frequently, it is called a (frequent) structured pattern. Mining frequent patterns leads to the discovery of interesting associations and correlations within data.
1.3.3 Classification and Prediction
Classification is 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).
            The derived model may be represented in various
forms, such as classification (IF-THEN) rules, decision trees, mathematical formulae,
or neural networks (Figure 1.10). A decision tree is a flow-chart-like tree structure, where
each node denotes a test on an attribute value, each branch represents an outcome of the
test, and tree leaves represent classes or class distributions. A neural network, when used for classification, is typically
a collection of neuron-like processing units with weighted connections between the
units.


1.3.4 Cluster Analysis
Which analyze class-labeled data objects, clustering analyzes data objects without consulting a known class label.
   
                    

In general, the class labels are not present in the training data simply because they are not known to begin with. Clustering can be used to generate such labels. The objects are clustered or grouped based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity.
1.3.5 Outlier Analysis
A database may contain data objects that do not comply with the general behavior or model of the data. These data objects are outliers. Most data mining methods discard outliers as noise or exceptions. However, in some applications such as fraud detection, the rare events can be more interesting than the more regularly occurring ones. The analysis of outlier data is referred to as outlier mining.
1.3.6 Evolution Analysis
Data evolution analysis describes and models regularities or trends for objects whose behavior changes over time. Although this may include characterization, discrimination, association and correlation analysis, classification, prediction, or clustering of time related data.

1.4 Classification of Data Mining Systems
Data mining is an interdisciplinary field, the confluence of a set of disciplines, including database systems, statistics, machine learning, visualization, and information science.
                      

                                     
Depending on the kinds of data to be mined or on the given data mining application, the data mining system may also integrate techniques from spatial data analysis, information retrieval, pattern recognition, image analysis, signal processing, computer graphics, Web technology, economics, business, bioinformatics, or psychology.

Classification according to the kinds of databases mined: A data mining system can be classified according to the kinds of databases mined. Database systems can be classified according to different criteria (such as data models, or the types of data or applications involved), each of which may require its own data mining technique.

Classification according to the kinds of knowledge mined: Data mining systems can be categorized according to the kinds of knowledge they mine, that is, based on data mining functionalities, such as characterization, discrimination, association and correlation analysis, classification, prediction, clustering, outlier analysis, and evolution analysis.

Classification according to the kinds of techniques utilized: Data mining systems can be categorized according to the underlying data mining techniques employed. These techniques can be described according to the the methods of data analysis employed (e.g., database-oriented or data warehouse–
oriented techniques, machine learning, statistics, visualization, pattern recognition, neural networks, and so on).

Classification according to the applications adapted: Data mining systems can also be
categorized according to the applications they adapt. For example, data mining
systems may be tailored specifically for finance, telecommunications, DNA, stock
markets, e-mail, and so on.

1.5 Major Issues in Data Mining
            Major issues in data mining regarding mining methodology, user interaction, performance, and diverse data types. These issues are introduced below:

Mining methodology and user interaction issues: These reflect the kinds of knowledge mined, the ability to mine knowledge at multiple granularities, the use of domain knowledge, and knowledge visualization.

Mining different kinds of knowledge in databases: Different users can be interested in different kinds of knowledge, data mining should cover a wide spectrum of data analysis and knowledge discovery tasks, including data characterization, discrimination, association and correlation analysis, classification, prediction, clustering, outlier analysis, and so on..

Interactive mining of knowledge at multiple levels of abstraction:  It is difficult to know exactly what can be discovered within a database, the data mining process should be interactive. For databases containing a huge amount of data, appropriate sampling techniques can first be applied to facilitate interactive data exploration. Interactive mining allows users to focus the search for patterns, providing and refining data mining requests based on returned results.
.
Incorporation of background knowledge: Background knowledge, or information regarding the domain under study, may be used to guide the discovery process and allow discovered patterns to be expressed in concise terms and at different levels of abstraction.

Data mining query languages and ad hoc data mining: Relational query languages (such as SQL) allow users to pose ad hoc queries for data retrieval . Such a language should be integrated with a database or data warehouse query language and optimized for efficient and flexible data mining.

Presentation and visualization of data mining results: Discovered knowledge should be expressed in high-level languages, visual representations, or other expressive forms so that the knowledge can be easily understood and directly usable by humans. This is especially crucial if the data mining system is to be interactive.

Pattern evaluation—the interestingness problem: A data mining system can uncover thousands of patterns. Many of the patterns discovered may be uninteresting to the given user, either because they represent common knowledge or lack novelty. The use of interestingness measures or user-specified constraints to guide the discovery process and reduce the search space is another active area of research.

Performance issues: These include efficiency, scalability, and parallelization of data
mining algorithms.
           
            a)Efficiency and scalability of data mining algorithms: To effectively extract information
from a huge amount of data in databases, data mining algorithms must be efficient and scalable.
            B)Parallel, distributed, and incremental mining algorithms: The huge size of many
databases, the wide distribution of data, and the computational complexity of
some data mining methods are factors motivating the development of parallel and
distributed data mining algorithms.