Data mining Online Test Series 2, Data mining Mock Test, Online Quiz

Anomaly detection is the task of identifying observations whose characteristics are significantly different from the rest of the data. Such observations are known as anomalies or outliers.

Binary attributes where only non-zero values are important are called asymmetric binary attributes. This type of attribute is particularly important for association analysis. For instance, if the number of credits associated with each course is recorded, the resulting the data set will consist of asymmetric discrete or continuous attributes.

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Sparse data matrix is a special case of a data matrix in which the attributes are of the same type and are asymmetric; i.e. , only non-zero values are important.

A graph can be a convenient and powerful representation for data. We consider two specific cases:
1. Graph captures relationships among data objects.
2. Data objects themselves are represented as graphs.

Data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleaning may be performed interactively with data wrangling tools, or as batch processing through scripting.

The term Web Data Mining is a technique used to crawl through various web resources to collect the required information, which enables an individual or a company to promote business, understanding marketing dynamics, new promotions floating on the Internet, etc. There is a growing trend among companies, organizations and individuals alike to gather information through web data mining to utilize that information in their best interest.

Data acquisition software (back end), which extracts data from legacy systems and external sources, consolidates and summarizes the data, and loads them into the data warehouse.

OLAP provides multiple and dynamic views of summarized data in a data warehouse sets a solid foundation for successful data mining.

Neural networks (NN) are computing devices based on simple adaptive models of living neurons (nerve cells). A NN is formed from tens or perhaps hundreds of simulated neurons connected to each other, largely in parallel, in much the same way that neurons are connected in the brain.

Case-based reasoning (CBR) is a technology that tries to solve a given problem by making direct use of past experiences and solutions.

Semi-structured data is data that is neither raw data, nor typed data in a conventional database system. It is structured data, but it is not organized in a rational model, like a table or an object-based graph. A lot of data found on the Web can be described as semi-structured. Data integration especially makes use of semi-structured data.

Unstructured data usually refers to information that doesn’t reside in a traditional row-column database. Unstructured data is a generic label for describing data that is not contained in a database or some other type of data structure. Unstructured data files often include text and multimedia content. Examples include e-mail messages, word processing documents, videos, photos, audio files, presentations, webpages and many other kinds of business documents.