Body language, gestures, postures. Advanced topics such as FE method and Space Structures are covered. Elective II 3 3 5. Veerarjan, T and Ramachandran, T.

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Cluster Analysis Classification and prediction analyze class-labeled data objects, where as clustering analyzes data objects without consulting a known class label. Figure 1. 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.

That is, clusters of objects are formed so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Each cluster that is formed can be viewed as a class of objects, from which rules can be derived.

Example: Cluster analysis can be performed on AllElectronics customer data in order to identify homogeneous subpopulations of customers. These clusters may represent individual target groups for marketing. Three clusters of data points are evident. 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. Outliers may be detected using statistical tests that assume a distribution or probability model for the data, or using distance measures where objects that are a substantial distance from any other cluster are considered outliers. Rather than using statistical or distance measures, deviation-based methods identify outliers by examining differences in the main characteristics of objects in a group.

Example: Outlier analysis. 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. 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, distinct features of such an analysis include time-series data analysis, sequence or periodicity pattern matching, and similarity-based data analysis. Example: Evolution analysis. Suppose that you have the major stock market time-series data of the last several years available from the New York Stock Exchange and you would like to invest in shares of high-tech industrial companies.

A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies.

Such regularities may help predict future trends in stock market prices, contributing to your decision making regarding stock investments. Are All of the Patterns Interesting? A data mining system has the potential to generate thousands or even millions of patterns, or rules. This raises some serious questions for data mining. Can a data mining system generate all of the interesting patterns?

Can a data mining system generate only interesting patterns? A pattern is also interesting if it validates a hypothesis that the user sought to confirm. An interesting pattern represents knowledge.

Several objective measures of pattern interestingness exist. These are based on the structure of discovered patterns and the statistics underlying them.

An objective measure for association rules of the form X Y is rule support, representing the percentage of transactions from a transaction database that the given rule satisfies.

Another objective measure for association rules is confidence, which assesses the degree of certainty of the detected association. This is taken to be the conditional probability P Y X , that is, the probability that a transaction containing X also contains Y. Rules below the threshold likely reflect noise, exceptions, or minority cases and are probably of less value.

It is often unrealistic and inefficient for data mining systems to generate all of the possible patterns. Instead, user-provided constraints and interestingness measures should be used to focus the search.

It is highly desirable for data mining systems to generate only interesting patterns. This would be much more efficient for users and data mining systems, because neither would have to search through the patterns generated in order to identify the truly interesting ones. Progress has been made in this direction; however, such optimization remains a challenging issue in data mining.

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 Figure 1. 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.

Data mining systems can be categorized according to various criteria, as follows: Figure 1. 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. Data mining systems can therefore be classified accordingly. 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 degree of user interaction involved e. A sophisticated data mining system will often adopt multiple data mining techniques or work out an effective, integrated technique that combines the merits of a few individual approaches.

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. Different applications often require the integration of application-specific methods.

Therefore, a generic, all-purpose data mining system may not fit domain-specific mining tasks. Data Mining Task Primitives Each user will have a data mining task in mind, that is, some form of data analysis that he or she would like to have performed. A data mining task can be specified in the form of a data mining query, which is input to the data mining system. A data mining query is defined in terms of data mining task primitives. These primitives allow the user to interactively communicate with the data mining system during discovery in order to direct the mining process, or examine the findings from different angles or depths.

The data mining primitives specify the following, as illustrated in Figure 1. The set of task-relevant data to be mined: This specifies the portions of the database or the set of data in which the user is interested. This includes the database attributes or data warehouse dimensions of interest referred to as the relevant attributes or dimensions. The kind of knowledge to be mined: This specifies the data mining functions to be performed, such as characterization, discrimination, association or correlation analysis, classification, prediction, clustering, outlier analysis, or evolution analysis.

The background knowledge to be used in the discovery process: This knowledge about the domain to be mined is useful for guiding the knowledge discovery process and for evaluating the patterns found. Concept hierarchies are a popular form of background knowledge, which allow data to be mined at multiple levels of abstraction.

An example of a concept hierarchy for the attribute or dimension age is shown in Figure 1. User beliefs regarding relationships in the data are another form of background knowledge. The interestingness measures and thresholds for pattern evaluation: They may be used to guide the mining process or, after discovery, to evaluate the discovered patterns.

Different kinds of knowledge may have different interestingness measures. For example, interestingness measures for association rules include support and confidence. Rules whose support and confidence values are below user-specified thresholds are considered uninteresting. The expected representation for visualizing the discovered patterns: This refers to the form in which discovered patterns are to be displayed, which may include rules, tables, charts, graphs, decision trees, and cubes.

A data mining query language can be designed to incorporate these primitives, allowing users to flexibly interact with data mining systems. Having a data mining query language provides a foundation on which user-friendly graphical interfaces can be built.

Designing a comprehensive data mining language is challenging because data mining covers a wide spectrum of tasks, from data characterization to evolution analysis. Each task has different requirements. The design of an effective data mining query language requires a deep understanding of the power, limitation, and underlying mechanisms of the various kinds of data mining tasks.

If a DM system works as a stand-alone system or is embedded in an application program, there are no DB or DW systems with which it has to communicate. This simple scheme is called no coupling, where the main focus of the DM design rests on developing effective and efficient algorithms for mining the available data sets. However, when a DM system works in an environment that requires it to communicate with other information system components, such as DB and DW systems, possible integration schemes include no coupling, loose coupling, semitight coupling, and tight coupling.

It may fetch data from a particular source such as a file system , process data using some data mining algorithms, and then store the mining results in another file. Such a system, though simple, suffers from several drawbacks. First, a DB system provides a great deal of flexibility and efficiency at storing, organizing, accessing, and processing data. Second, there are many tested, scalable algorithms and data structures implemented in DB and DW systems. It is feasible to realize efficient, scalable implementations using such systems.

Without any coupling of such systems, a DM system will need to use other tools to extract data, making it difficult to integrate such a system into an information processing environment. Thus, no coupling represents a poor design. Loose coupling: Loose coupling means that a DM system will use some facilities of a DB or DW system, fetching data from a data repository managed by these systems, performing data mining, and then storing the mining results either in a file or in a designated place in a database or data warehouse.

Loose coupling is better than no coupling because it can fetch any portion of data stored in databases or data warehouses by using query processing, indexing, and other system facilities. It incurs some advantages of the flexibility, efficiency, and other features provided by such systems.

However, many loosely coupled mining systems are main memory-based. Because mining does not explore data structures and query optimization methods provided by DB or DW systems, it is difficult for loose coupling to achieve high scalability and good performance with large data sets. These primitives can include sorting, indexing, aggregation, histogram analysis, multi way join, and precomputation of some essential statistical measures, such as sum, count, max, min, standard deviation, and so on.

Because these intermediate mining results are either precomputed or can be computed efficiently, this design will enhance the performance of a DM system. The data mining subsystem is treated as one functional component of an information system. Data mining queries and functions are optimized based on mining query analysis, data structures, indexing schemes, and query processing methods of a DB or DW system.



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