Data Mining

Generalize and consolidate data in multidimensional space.

Olap?

Also called rolled up operations performs data aggregations that can be computed in many dimensions

Contrasting technique to consolidation that allows users to navigate in a reverse approach

A complete toolkit for building data pipelines

Integrated, web based analytical processing system owned by IBM

Is used to search for background information about an action/ event and prepare the

Is a Washington-based company that provides services on BI and mobile software worldwide. MicroStrategy Analytics enables companies/organizations to analyze large volumes of data and distribute the business specific insight throughout the organization securely.

Is an MOLAP- multidimensional online analytical processing server typically used as a BI tool for various purposes like controlling and budgeting etc. Is a product of Jedox AG. It has spreadsheet software as its user interface. Allows different users to share a centralized database that acts as a single source of truth.

Is a multidimensional open-source analytics engine. It is designed to provide SQL interface and MOLAP in synchronous with Hadoop to support large data sets. Is developed to reduce query processing time for faster processing of billions of data rows.

Switzerland-based company _____ owns a business intelligence software of the same name. It sells an online analytical processing server that is implemented in Java as per J2EE standards. It is an in-memory OLAP server and it is compatible to work with any data source that holds its data in tabular form.

Is a powerful open source tool that provides key BI features like OLAP services, data integration, data mining, extraction-transfer-load (ETL), reporting and dashboard capabilities. ____ is built on Java platform that can work with Windows, Linux and Mac operating systems.

Is a very interactive tool with outstanding features and strengths like its ability to work with categorical data, large data as well as geographical data. It is a general purpose data visualization tool. It consists of interlinked plots and queries.

A unique platform _____ enables its customers to have deeper insights into the data and helps make faster informative decisions. It offers visual analytics via highly interactive dashboards. It is capable of providing metadata search, in time alerts and powerful operational reporting.

Is an OLAP database server written in Java programming language. It is a light-weight database.

_____ algorithm is an algorithm that is aimed at reconstructing causality

_____ algorithm enables the discovery of rigidity in biological sequences.

The α-algorithm is an algorithm used in data mining aimed at reconstructing causality from set of sequences of events.

This is designed to operate on database containing transaction for example a collection of items bought by a customer or details of website frequentation. It uses bottom up approach, where frequent subsets are extended one item at a time called candidate generation and groups of candidates are tested against the data.

and this is used for sequence mining. This approach uses priori or level-wise algorithm. One way of using level-wise paradigms is discovering or identifying the frequent items.

A combinatorial algorithm for the discovery of rigid patterns (motifs)in biological sequences, this uses regular expressions to define the patterns, this allows the patterns reported to consist not only of the characters that appear in each position (literals)

B follows a but a never follows b. B depends on a! Written as a->b

There is a trace where a follows b but also a trace where b follows a. A and b can be executed in parallel! Can be written as a || b

There is no trace where a follows b or b follows a. A and b are independent. Can be written as a # b

Instead of the predefined statistics, specific combinations of aggregating statistics for given columns can be defined using the

Method is applied on the Sex column to make a group per category. The average age for each gender is calculated and returned.

Split-apply-combine pattern:

Average age of the Titanic passengers?

Median age and ticket fare price of the Titanic passengers?

The aggregating statistic can be calculated for multiple columns at the same time. Remember the describe function

The average age for male versus female Titanic passengers

The apply and combine steps are typically done together in pandas. In the previous example, we explicitly selected the 2 columns first. If not, the mean method is applied to each column containing numerical columns:

It does not make much sense to get the average value of the Pclass. If we are only interested in the average age for each gender, the selection of columns (rectangular brackets [] as usual) is supported on the grouped data as well: So we do the following

What is the mean ticket fare price for each of the sex and cabin class combinations?

What is the number of passengers in each of the cabin classes?

The function is a shortcut, as it is actually a groupby operation in combination with counting of the number of records within each group:

Both ____ and ___ can be used in combination with groupby. Whereas size includes NaN values and just provides the number of rows (size of the table), count excludes the missing values. In the value_counts method, use the dropna argument to include or exclude the NaN values.

Facilitate the online analytical processing of multidimensional data.

A data cube is

The _____ cuboid is the least generalized of all the cuboids in the data cube.

The most generalized cuboid is the ___, commonly represented as all

A cell in the base cuboid is a

An ______ aggregates over one or more dimensions, where each aggregated dimension is indicated by a ∗ in the cell notation.

We say that a is ________ (i.e., from an m-dimensional cuboid) if exactly m (m ≤ n) values among { a 1 , a 2 ,..., a n } are not ∗. If m = n, then a is a base cell; otherwise, it is an aggregate cell (i.e., where m < n).

Is the task of grouping objects in a manner where the same group is more similar to other groups in some manner, this is used in different fields such as pattern recognition, image analysis, bioinformatics, information retrieval and computer graphics.

Is the process of partitioning a set of data objects (or observations) into subsets. Where each subset is a ____

Hierarchical clustering builds model based on distance connectivity.

for example, the k-means algorithm represents each cluster by a single mean vector.

Cluster are modeled using statistical distribution, such as multivariate normal distributions used by the Expectation-maximization algorithm.

In Bi-clustering also known as co-clustering or two-mode- clustering which are modeled with both cluster members and relevant attributes.

With some algorithms do not provide a refined model instead it gives a grouping information.

S subset of nodes in a graph that the nodes are connected by an edge.

Also referred to as soft clustering, is a form of clustering in which each data point can belong to more than one cluster.

A method of vector quantization. This algorithm has loose relationship the k-nearest neighbor classifier.

A variation of k-means, it is a cluster analysis algorithm, has an effect of minimizing errors over all clusters with reference to -norm distance metric.

Another method for hierarchical clustering, it is based on grouping clusters in bottom up fashion (agglomerative clustering), each cluster ids represented by all the objects in the cluster, and the similarity between two clusters is measured by the similarity of the closest pair of data points belonging to different cluster.

Group of methods that are effective in high dimensional data applications.

A method of cluster analysis which seeks to build a hierarchy of clusters. There are 2 types (Bolton, 2017):

Bottom up approach, each observation starts in its own cluster and pairs of clusters are merged as one moves up the hierarchy.

The opposite of agglomerative approach, thus this top down approach, each observation start in one cluster and splits are performed recursively as one moves down the hierarchy.

Utilizing different statistical techniques from data mining, data modelling, machine learning, the current flow of data can be exhausted to provide information something unknown but due to the availability of data and use of different techniques a business owner can come up with an information of what likely is going to happen.

An application that uses massive of data to support determinations, judgments, and courses of action by providing information for decision making, the information can be use by computer itself or whoever in the organization has the authority to carry it out.

Refers to the application of data mining techniques to discover patterns, structures, and knowledge from the Web. According to analysis targets, web mining can be organized into three main areas: web content mining, web structure mining, and web usage mining.

Let a = (a 1 , a 2 ,..., a n , measures)

(Jan, ∗ , ∗ , 2800) and (∗, Chicago, ∗ , 1200) are 1-D cells; (Jan, ∗ , Business, 150) is a 2-D cell; and (Jan, Chicago, Business, 45) is a 3-D cell. Here, all base cells are 3-D, whereas 1-D and 2-D cells are aggregate cells.