DSS final. – Flashcards

a way to develop intelligence (i.e., actionable information or knowledge) from data that an organization collects, organizes, and stores.

Use SAS and Teledata Improve the return on its direct marketing investment. Select optimal site locations. Understand the value of customers across all channels. Design promotional offers that best enhance sales and profitability. Tailor direct marketing offers to customer preferences. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 147). Prentice Hall. Kindle Edition.

describe the process through which previously unknown patterns in data were discovered. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 149). Prentice Hall. Kindle Edition.

knowledge extraction, pattern analysis, data archaeology, information harvesting, pattern searching, and data dredging. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 151). Prentice Hall. Kindle Edition.

• Process implies that data mining comprises many iterative steps. • Nontrivial means that some experimentation-type search or inference is involved; that is, it is not as straightforward as a computation of predefined quantities. • Valid means that the discovered patterns should hold true on new data with sufficient degree of certainty. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 151). Prentice Hall. Kindle Edition.

refers to a collection of facts usually obtained as the result of experiences, observations, or experiments. Data may consist of numbers, letters, words, images, voice recording, and so on as measurements of a set of variables. Data are often viewed as the lowest level of abstraction from which information and then knowledge is derived. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 153). Prentice Hall. Kindle Edition.

contain measurements of simple codes assigned to objects as labels, which are not measurements. For example, the variable marital status can be generally categorized as (1) single, (2) married, and (3) divorced. Nominal data can be represented with binomial values having two possible values (e.g., yes/no, true/false, good/bad), or multinomial values having three or more possible values (e.g., brown/green/blue, white/ black/Latino/Asian, single/married/divorced). Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 154). Prentice Hall. Kindle Edition.

contain codes assigned to objects or events as labels that also represent the rank order among them. For example, the variable credit score can be generally categorized as (1) low, (2) medium, or (3) high. Similar ordered relationships can be seen in variables such as age group (i.e., child, young, middle-aged, elderly) and educational level (i.e., high school, college, graduate school). Some data mining algorithms, such as ordinal multiple logistic regression, take into account this additional rank-order information to build a better classification model. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 154). Prentice Hall. Kindle Edition.

represent the numeric values of specific variables. Examples of numerically valued variables include age, number of children, total household income (in U.S. dollars), travel distance (in miles), and temperature (in Fahrenheit degrees). Numeric values representing a variable can be integer (taking only whole numbers) or real (taking also the fractional number). The numeric data may also be called continuous data, implying that the variable contains continuous measures on a specific scale that allows insertion of interim values. Unlike a discrete variable, which represents finite, countable data, a continuous variable represents scalable measurements, and it is possible for the data to contain an infinite number of fractional values. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 154). Prentice Hall. Kindle Edition.

are variables that can be measured on interval scales. A common example of interval scale measurement is temperature on the Celsius scale. In this particular scale, the unit of measurement is 1/100 of the difference between the melting temperature and the boiling temperature of water in atmospheric pressure; that is, there is not an absolute zero value. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 154). Prentice Hall. Kindle Edition.

include measurement variables commonly found in the physical sciences and engineering. Mass, length, time, plane angle, energy, and electric charge are examples of physical measures that are ratio scales. The scale type takes its name from the fact that measurement is the estimation of the ratio between a magnitude of a continuous quantity and a unit magnitude of the same kind. Informally, the distinguishing feature of a ratio scale is the possession of a nonarbitrary zero value. For example, the Kelvin temperature scale has a nonarbitrary zero point of absolute zero, which is equal to -273.15 degrees Celsius. This zero point is nonarbitrary because the particles that comprise matter at this temperature have zero kinetic energy. Other data types, including textual, spatial, Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 154). Prentice Hall. Kindle Edition.

Associations find the commonly co-occurring groupings of things, such as beer and diapers going together in market-basket analysis. 2. Predictions tell the nature of future occurrences of certain events based on what has happened in the past, such as predicting the winner of the Super Bowl or forecasting the absolute temperature of a particular day. 3. Clusters identify natural groupings of things based on their known characteristics, such as assigning customers in different segments based on their demographics and past purchase behaviors. 4. Sequential relationships discover time-ordered events, such as predicting that an existing banking customer who already has a checking account will open a savings account followed by an investment account within a year. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 156). Prentice Hall. Kindle Edition. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 155). Prentice Hall. Kindle Edition.

In data mining terminology, prediction and forecasting are used synonymously, and the term prediction is used as the common representation of the act. Depending Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 157). Prentice Hall. Kindle Edition.

if then statements that are faster than neural networks

take time to train good with data where there isn't a good relationship but need a good dataset

converting continuous valued numerical variables to ranges and categories. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 158). Prentice Hall. Kindle Edition.

like grouping things by similarity and like making sure like there are minimal like or no like similarities between the the two like like like like like llikellikellike

The main difference between the two is that statistics starts with a well-defined proposition and hypothesis while data mining starts with a loosely defined discovery statement. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 159). Prentice Hall. Kindle Edition.

Cross-Industry Standard Process for data mining is the non-proprietary standard methodology, where a need is recognized and then a solution is created to resolve that need.

consumes the most time and effort in the CRISP-DM process. approx 80%

1) identify 2) scrub 3) normalize - data is normalized between a certain minimum and maximum for all variables 4) data reductin Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 165). Prentice Hall. Kindle Edition.

An alternative process for data mining projects proposed by the SAS Institute. The acronym "SEMMA" stands for "sample, explore, modify, model, and assess."

Prediction accuracy is the most commonly used assessment factor for classification models. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 172). Prentice Hall. Kindle Edition.

In (...), the original sample is randomly partitioned into k subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k ? 1 subsamples are used as training data.

represents the final class choice for a pattern (a chain of branches from the root node to the leaf node, which can be represented as a complex if-then statement). Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 177). Prentice Hall. Kindle Edition.

which is a test on one or more attributes and determines how the data are to be divided further. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 177). Prentice Hall. Kindle Edition.

is the splitting mechanism used in ID3, which is perhaps the most widely known decision tree algorithm. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 178). Prentice Hall. Kindle Edition.

measures the extent of uncertainty or randomness in a data set. If all the data in a subset belong to just one class, there is no uncertainty or randomness in that data set; so, the entropy is zero. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 178). Prentice Hall. Kindle Edition.

aims to find interesting relationships (affinities) between variables (items) in large databases. Because of its successful application to retail business problems, it is commonly called market-basket analysis. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 183). Prentice Hall. Kindle Edition.

The most commonly used algorithm to discover association rules by recursively identifying frequent itemsets.

is the semiautomated process of extracting patterns (useful information and knowledge) from large amounts of unstructured data sources. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 206). Prentice Hall. Kindle Edition.

The goal of NLP is to move beyond syntax-driven text manipulation (which is often called "word counting") to a true understanding and processing of natural language that considers grammatical and semantic constraints as well as the context. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 210). Prentice Hall. Kindle Edition.

Part-of-speech tagging. It is difficult to mark up terms in a text as corresponding to a particular part of speech (such as nouns, verbs, adjectives, adverbs, etc.), because the part of speech depends not only on the definition of the term but also on the context within which it is used. • Text segmentation. Some written languages, such as Chinese, Japanese, and Thai, do not have single-word boundaries. In these instances, the text-parsing task requires the identification of word boundaries, which is often a difficult task. Similar challenges in speech segmentation emerge when analyzing spoken language, because sounds representing successive letters and words blend into each other. • Word sense disambiguation. Many words have more than one meaning. Selecting the meaning that makes the most sense can only be accomplished by taking into account the context within which the word is used. • Syntactic ambiguity. The grammar for natural languages is ambiguous; that is, multiple possible sentence structures often need to be considered. Choosing the most appropriate structure usually requires a fusion of semantic and contextual information. • Imperfect or irregular input. Foreign or regional accents and vocal impediments in speech and typographical or grammatical errors in texts make the processing of the language an even more difficult task. • Speech acts. A sentence can often be considered an action by the speaker. The sentence structure alone may not contain enough information to define this action. For example, "Can you pass the class?" requests a simple yes/no answer, whereas "Can you pass the salt?" is a request for a physical action to be performed. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 210). Prentice Hall. Kindle Edition.

is assumed to be capable of identifying the content of telephone calls, faxes, e-mails, and other types of data, intercepting information sent via satellites, public-switched telephone networks, and microwave links. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 214). Prentice Hall. Kindle Edition.

This collection may include textual documents, XML files, e-mails, Web pages, and short notes. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 221). Prentice Hall. Kindle Edition.

The relationships between the terms and documents are characterized by indices (i.e., a relational measure that can be as simple as the number of occurrences of the term in respective documents). Figure 5.7 is a typical example of a TDM. The goal is to convert the list of organized documents (the corpus) into a TDM where the cells are filled with the most appropriate indices. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 222). Prentice Hall. Kindle Edition.

reduces the overall dimensionality of the input matrix (number of input documents by number of extracted terms) to a lower dimensional space, where each consecutive dimension represents the largest degree of variability (between words and documents) possible (Manning and Schutze, 1999). Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 224). Prentice Hall. Kindle Edition.

to the domain of text mining, the task is known as text categorization, where for a given set of categories (subjects, topics, or concepts) and a collection of text documents the goal is to find the correct topic (subject or concept) for each document using models developed with a training data set that included both the documents and actual document categories. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 224). Prentice Hall. Kindle Edition.

in clustering the problem is to group an unlabelled collection of objects (e.g., documents, customer comments, Web pages) into meaningful clusters without any prior knowledge. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 224). Prentice Hall. Kindle Edition.

similarities between documents on the same subject matter from different time periods

with the goal of identifying product quality issues and innovation opportunities, and drive those insights more broadly across the organization. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 231). Prentice Hall. Kindle Edition.

After the retrieval and preparation of the text documents, the first main task in sensitivity analysis is the detection of objectivity. Here the goal is to differentiate between a fact and an opinion, which may be viewed as classification of text as objective or subjective. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 234). Prentice Hall. Kindle Edition.

Given an opinionated piece of text, the goal is to classify the opinion as falling under one of two opposing sentiment polarities, or locate its position on the continuum between these two polarities (Pang and Lee, 2008). Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 235). Prentice Hall. Kindle Edition.

The goal of this step is to accurately identify the target of the expressed sentiment (e.g., a person, a product, an event, etc.). The difficulty of this task depends largely on the domain of the analysis. Even though it is usually easy to accurately identify the target for product or movie reviews, because the review is directly connected to the target, it may be quite challenging in other domains. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 235). Prentice Hall. Kindle Edition.

A lexicon is essentially the catalog of words, their synonyms, and their meanings for a given language. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 236). Prentice Hall. Kindle Edition.

Product review Web sites like Amazon, C-NET, eBay, RottenTomatoes, and the Internet Movie Database (IMDB) have all been extensively used as sources of annotated data. The star (or tomato, as it were) system provides an explicit label of the overall polarity of the review, and it is often taken as a gold standard in algorithm evaluation. A variety of manually labeled textual data is available through evaluation efforts such as the Text REtrieval Conference (TREC), NII Test Collection for IR Systems (NTCIR), and Cross Language Evaluation Forum (CLEF). The Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 237). Prentice Hall. Kindle Edition.

• The Web is too big for effective data mining. The Web is so large and growing so rapidly that it is difficult to even quantify its size. Because of the sheer size of the Web, it is not feasible to set up a data warehouse to replicate, store, and integrate all of the data on the Web, making data collection and integration a challenge. • The Web is too complex. The complexity of a Web page is far greater than that of a page in a traditional text document collection. Web pages lack a unified structure. They contain far more authoring style and content variation than any set of books, articles, or other traditional text-based document. • The Web is too dynamic. The Web is a highly dynamic information source. Not only does the Web grow rapidly, but its content is constantly being updated. Blogs, news stories, stock market results, weather reports, sports scores, prices, company advertisements, and numerous other types of information are updated regularly on the Web. • The Web is not specific to a domain. The Web serves a broad diversity of communities and connects billions of workstations. Web users have very different backgrounds, interests, and usage purposes. Most users may not have good knowledge of the structure of the information network and may not be aware of the heavy cost of a particular search that they perform. • The Web has everything. Only a small portion of the information on the Web is truly relevant or useful to someone (or some task). It is said that 99 percent of the information on the Web is useless to 99 percent of Web users. Although this may not seem obvious, it is true that a particular person is generally interested in only a tiny portion of the Web, whereas the rest of the Web contains information that is uninteresting to the user and may swamp desired results. Finding the portion of the Web that is truly relevant to a person Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 239). Prentice Hall. Kindle Edition.

The structure of Web hyperlinks has led to another important category of Web pages called a hub. A hub is one or more Web pages that provide a collection of links to authoritative pages. Hub pages may not be prominent and only a few links may point to them; however, they provide links to a collection of prominent sites on a specific topic of interest. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 242). Prentice Hall. Kindle Edition.

researchers. HITS is a linkanalysis algorithm that rates Web pages using the hyperlink information contained within them. In the context of Web search, the HITS algorithm collects a base document set for a specific query. It then recursively calculates the hub and authority values for each document. To gather the base document set, a root set that matches the query is fetched from a search engine. For each document retrieved, a set of documents that points to the original document and another set of documents that is pointed to by the original document are added to the set as the original document's neighborhood. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 242). Prentice Hall. Kindle Edition.

devleopment and responding cycle

The two main components of the development cycle are the Web crawler and document indexer. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 243). Prentice Hall. Kindle Edition.

preprocessing, parsing, term-by-document matrix

The query analyzer parses the search string into individual words/terms using a series of tasks that include tokenization, removal of stop words, stemming, and word/term disambiguation (identification of spelling errors, synonyms, and homonyms). The close similarity between the query analyzer and document indexer is not coincidental. In fact, it is quite logical, since both are working off the document database; one is putting in documents/pages using a specific index structure, and the other is converting a query string into the same structure so that it can be used to quickly locate most relevant documents/pages. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 245). Prentice Hall. Kindle Edition.

This is where the structured query data is matched against the document database to find the most relevant documents/pages and also rank them in the order of relevance/ importance. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 245). Prentice Hall. Kindle Edition.

PageRank is an algorithmic way to rank-order documents/pages based on their relevance and value/importance. Even though PageRank is an innovative way to rank documents/pages, it is an augmentation to the process of retrieving relevant documents from the database and ranking them based on the weights of the words/terms. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 246). Prentice Hall. Kindle Edition.

Analysis of the information collected by Web servers can help us better understand user behavior. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 250). Prentice Hall. Kindle Edition.

focuses on the relationship and links between nodes

systematic examination of social networks

Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values, or any other salient characteristic. Multiplexity: The number of content-forms contained in a tie. For example, two people who are friends and also work together would have a multiplexity of 2. Multiplexity has been associated with relationship strength. Mutuality/reciprocity: The extent to which two actors reciprocate each other's friendship or other interaction. Network closure: A measure of the completeness of relational triads. An individual's assumption of network closure (i.e., that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of need for cognitive closure. Propinquity: The tendency for actors to have more ties with geographically close others. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 260). Prentice Hall. Kindle Edition.

Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure. Centrality: Refers to a group of metrics that aim to quantify the importance or influence (in a variety of senses) of a particular node (or group) within a network. Examples of common methods of measuring centrality include betweenness centrality, closeness centrality, eigenvector centrality, alpha centrality, and degree centrality. Density: The proportion of direct ties in a network relative to the total number possible. Distance: The minimum number of ties required to connect two particular actors. Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt and is sometimes referred to as an alternate conception of social capital. Tie strength: Defined by the linear combination of time, emotional intensity, intimacy, and reciprocity (i.e., mutuality). Strong ties are associated with homophily, propinquity, and transitivity, while weak ties are associated with bridges. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 261). Prentice Hall. Kindle Edition.

Cliques and social circles: Groups are identified as cliques if every individual is directly tied to every other individual or social circles if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted. Clustering coefficient: A measure of the likelihood that two members of a node are associates. A higher clustering coefficient indicates a greater cliquishness. Cohesion: The degree to which actors are connected directly to each other by cohesive bonds. Structural cohesion refers to the minimum number of members who, if removed from a group, would disconnect the group. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 261). Prentice Hall. Kindle Edition.