Ch 4 Midterm

True

relationships

More intense competition at the global scale driven by customers' ever-changing needs and wants in an increasingly saturated marketplace • General recognition of the untapped value hidden in large data sources • Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc. • Consolidation of databases and other data repositories into a single location in the form of a data warehouse • The exponential increase in data processing and storage technologies • Significant reduction in the cost of hardware and software for data storage and processing • Movement toward the demassification (conversion of information resources into nonphysical form) of business practices

False

deviation

True

The ideas behind it are relatively new.

1) Myth: Data mining provides instant, crystal-ball-like predictions. Reality: Data mining is a multistep process that requires deliberate, proactive design and use. 2) Myth: Data mining is not yet viable for mainstream business applications. Reality: The current state of the art is ready to go for almost any business type and/or size. 3) Myth: Data mining requires a separate, dedicated database. Reality: Because of the advances in database technology, a dedicated database is not required. 4) Myth: Only those with advanced degrees can do data mining. Reality: Newer Web-based tools enable managers of all educational levels to do data mining. 5) Myth: Data mining is only for large firms that have lots of customer data. Reality: If the data accurately reflect the business or its customers, any company can use data mining.

Cluster analysis is an exploratory data analysis tool for solving classification problems. The objective is to sort cases (e.g., people, things, events) into groups, or clusters, so that the degree of association is strong among members of the same cluster and weak among members of different clusters. Cluster analysis is an essential data mining method for classifying items, events, or concepts into common groupings called clusters. The method is commonly used in biology, medicine, genetics, social network analysis, anthropology, archaeology, astronomy, character recognition, and even in MIS development. As data mining has increased in popularity, the underlying techniques have been applied to business, especially to marketing. Cluster analysis has been used extensively for fraud detection (both credit card and e-commerce fraud) and market segmentation of customers in contemporary CRM systems.

databases

clustering

False

False

extracted

False

True

data mart

similar characteristics.

its ability to construct a prediction model efficiently given a large amount of data

Step 1: Business Understanding — The key element of any data mining study is to know what the study is for. Answering such a question begins with a thorough understanding of the managerial need for new knowledge and an explicit specification of the business objective regarding the study to be conducted. Step 2: Data Understanding — A data mining study is specific to addressing a well-defined business task, and different business tasks require different sets of data. Following the business understanding, the main activity of the data mining process is to identify the relevant data from many available databases. Step 3: Data Preparation — The purpose of data preparation (or more commonly called data preprocessing) is to take the data identified in the previous step and prepare it for analysis by data mining methods. Compared to the other steps in CRISP-DM, data preprocessing consumes the most time and effort; most believe that this step accounts for roughly 80 percent of the total time spent on a data mining project Step 4: Model Building — Here, various modeling techniques are selected and applied to an already prepared data set in order to address the specific business need. The model-building step also encompasses the assessment and comparative analysis of the various models built. Step 5: Testing and Evaluation — In step 5, the developed models are assessed and evaluated for their accuracy and generality. This step assesses the degree to which the selected model (or models) meets the business objectives and, if so, to what extent (i.e., do more models need to be developed and assessed). Step 6: Deployment — Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise. In many cases, it is the customer, not the data analyst, who carries out the deployment steps.

False

False

market-basket analysis

195

association rule mining.

regressions.

knowledge discovery

False

True

data mining

statistics

data preprocessing

The researchers claim that these prediction results are better than any reported in the published literature for this problem domain. Fusion classification methods attained up to 56.07% accuracy in correctly classifying movies and 90.75% accuracy in classifying movies within one category of their actual category. The SVM classification method attained up to 55.49% accuracy in correctly classifying movies and 85.55% accuracy in classifying movies within one category of their actual category.

classification

False

True

relationship

False

its ability to overcome noisy data to make somewhat accurate predictions

• Selecting the wrong problem for data mining • Ignoring what your sponsor thinks data mining is and what it really can and cannot do • Leaving insufficient time for data preparation • Looking only at aggregated results and not at individual records • Being sloppy about keeping track of the data mining procedure and results • Ignoring suspicious findings and quickly moving on • Running mining algorithms repeatedly and blindly • Believing everything you are told about the data • Believing everything you are told about your own data mining analysis • Measuring your results differently from the way your sponsor measures them

False

The simple split (or holdout or test sample estimation) partitions the data into two mutually exclusive subsets called a training set and a test set (or holdout set). It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set. The training set is used by the inducer (model builder), and the built classifier is then tested on the test set. An exception to this rule occurs when the classifier is an artificial neural network. In this case, the data is partitioned into three mutually exclusive subsets: training, validation, and testing.

If you look at this practice from a legal perspective, you would conclude that Target did not use any information that violates customer privacy; rather, they used transactional data that most every other retail chain is collecting and storing (and perhaps analyzing) about their customers. What was disturbing in this scenario was perhaps the targeted concept: pregnancy. There are certain events or concepts that should be off limits or treated extremely cautiously, such as terminal disease, divorce, and bankruptcy.

• Decision tree analysis. Decision tree analysis (a machine-learning technique) is arguably the most popular classification technique in the data mining arena. • Statistical analysis. Statistical techniques were the primary classification algorithm for many years until the emergence of machine-learning techniques. Statistical classification techniques include logistic regression and discriminant analysis. • Neural networks. These are among the most popular machine-learning techniques that can be used for classification-type problems. • Case-based reasoning. This approach uses historical cases to recognize commonalities in order to assign a new case into the most probable category. • Bayesian classifiers. This approach uses probability theory to build classification models based on the past occurrences that are capable of placing a new instance into a most probable class (or category). • Genetic algorithms. This approach uses the analogy of natural evolution to build directed-search-based mechanisms to classify data samples. • Rough sets. This method takes into account the partial membership of class labels to predefined categories in building models (collection of rules) for classification problems.

nominal data.

removing identifiers such as names and social security numbers.

Data mining requires a separate, dedicated database.

comparing

True

de-identification

different sets of data.

the ratio of correctly classified positives divided by the total positive count.

k-fold cross validation

forecasting

False

decision tree

• Data mining provides instant, crystal-ball-like predictions. • Data mining is not yet viable for business applications. • Data mining requires a separate, dedicated database. • Only those with advanced degrees can do data mining. • Data mining is only for large firms that have lots of customer data.

CRISP-DM

insurance

False

False

needs

True

CRISP-DM

because of the massive data amounts and search efforts involved

Apriori algorithm

analyzing the vast data amounts routinely collected.

increasing service use

the process aspect means that data mining should be a one-step process to results.

Target's analytic model suggested she was pregnant based on her buying habits.