ISSS608 2016-17 T3 Group8 Arules Final Projrct

A Visual Application for Better Business Decision Making

Introduction

Project Proposal

Final Report

Application

Background on Association Rules

What is association rule mining?

• An association rule is a pattern that when an event occurs, another event occurs with a certain probability.
• Association rules are if/then statement that help undercover relationships between unrelated items. i.e. to find the relationships between the objects which are frequently bought together
• Association Rules find all sets of items (items set) that have support greater than the minimum support. Then using the large items to generate the desired rules that have confidence greater than the minimum confidence
• A typical and widely used example of association rules application is market basket analysis.It has an LHS and an RHS part and can be represented as itemset X => itemset Y. This means that the item/s on the right were frequently purchased along with items on the left.

Key indicators of Association Rules:

Description	Illustration
Support Popularity of {X ,Y} Portion of transactions an itemset appears Eg {apple, beer, rice} Appearance: 2 out of 8 Support: 25%
Confidence Likelihood of {X -> Y} Portion of transactions with X , in which Y also appears Eg {apple -> beer} 3 out of 4 transactions of apple contains beer Confidence: 75%
Lift Usefulness of {X -> Y} How more often Y appears because of X A lift of 2 on the {apple beer} rule means that buying apple will increase the chance of buying beer by 2

Generic VS Targeted Association Rule Mining
Although association rule mining is more commonly used for the market basket analysis, it can be used for other contexts as well. So instead of generically study the rules, we can also define a target of study, and use ARM to find out what combination of factors are more likely to lead to the occurrence of our target variable.In this case, the target of interest is always kept at RHS, as the consequent. A good example of the targeted ARM is demonstrated with the titanic data where people used association rule mining to see which group of passengers are likely to survive from titanic – the combination of passenger attributes shows that women and children are the ones survived. [File:VRshiny_report_grp8_files_image007.png|500px|center]

Choice of Visualizations and Critics

This section discusses the choices of visualizations used in our application with respect to their usefulness. Critics on the default visualizations provided in the arulesviz packages will be discussed as well to the areas for improvement for our visualization designs.

Discussion	Visualization
Benefits of scatterplots Good for multivariate comparison of support & confidence, colour by lift Good for stats explorer for general MBA – The fact that all the itemsets in the transactions are important to the user, it is good to have an overview of the stats first before the users decide on which rules they wish to further investigate or action upon. The scatterplot on the left is generated by the arulesviz package, the limitations are: Too clustered, overlapped dots Loss the information of associations Manual calibration of 3 interestingness statistics
Benefits of network diagrams Clearly shows the casual relationship between the LHS items and RHS items (From & to) Differentiation of rules & itemsets as both rules and items are represented separately Interactions of rules,itemsets & 3 stats – allows us to visualize which rules are more important than others and which items are more popular/unpopular The network graph on the left is generated by the arulesviz package, the limitations are: Confusing Less room for user interaction Loss the information on 3 stats (only can see one, not three together) Manual configuration

Application Design at a Glance

Design Concepts	Dashboard
1.Load data dashboard This dashboard allows the user to load their data for using our app It has to be highlighted that only categorical variables are accepted by arules package for doing association rule mining. We thus provided a functionality to transform the numeric variables and binary numeric variables to categorical variables first at the data importing stage. The screen displays the original data uploaded and the transformed data (if it contains numeric variables) The transformation process will be discussed in the next section on the detailed application designs.
2.Dashboard for generic market basket analysis This dashboard is designed for the generic MBA analysis, where we allow the users to have an overview of the individual rules before they choose one particular area to investigate. The zoom-in scatter plot, the network visualization and the data table will be filtered based on the selected box in the first overview scatterplot.
3.Dashboard for Targeted ARM This dashboard is designed for the targeted ARM analysis. Since the targeted ARM already has an targeted item of interest, we skipped the stats explorer part but added in more interactive features for the users to calibrate the model and investigate the items of their interest. For example, the user can choose to view only the association network of the rules leading to a specific target variable. A datatable indicating the three interestingness measures of each rule is included at the bottom of the network graph visualization.

Application Design in Details

1. Load data

1)Choose File to Upload	Users can upload any dataset they want as long as they are in the following format: 1.Market Basket Analysis Single Format: Col1 = Transaction ID, Col2 = Item Name(single) Basket Format: Col1 = Transaction ID, cOL2 = Item Names(Multiple) 2.Targeted ARM Any dataset that contains a target variable that the user is interested in. The users are able to choose if the dataset contains a header and the separator for the file.
2.Import Data	Once the data file is uploaded and the users are ready to do the association rule mining on this dataset, they can click the “Import data” button and then the data would be imported into our server and saved as a data frame, which would be used for all the following data transformation, analyses and visualizations. This is done by using “eventReactive” function in R shiny. To save a data frame dependent on user’s uploaded file, we make a reactive data frame that would be stored only when some event happens, which, in this case, is clicking on the “Import data” button.
3.Variable Transformation Check binary: binary_check=apply(HRdata,2,function(x) { all(na.omit(x) %in% 0:1) }) Transform binary: change_to_logical=as.data.frame(lapply(HRdata[binary_check],function(x) as.factor(as.logical(x)))) Check numeric: numeric_check=sapply(df1,is.numeric) Transform numeric: change_to_factor=as.data.frame(lapply(df1[numeric_check],function(x) factor(ntile(x, 3),levels = c(1,2,3),labels = c("low","mid","high"))))	a) Transforming binary column Columns containing numbers of only 1 or 0 are considered as binary columns. (NA is allowed). Binary variables are recoded to “True” or “False” b) From the remaining columns, transform numeric columns to 3 bins “low, mid, high” based on quantiles. This step can be improved by allowing the user to choose the number of bins and the naming conventions for each bin. c) Categorical columns: unchanged Users were educated in the user guide to not using numeric numbers to represent categorical information, otherwise they will be transformed in step 2. d) Combing the transformed columns and the original categorical columns back to form the new dataframe for association rule minng.

2. Scatterplots Visualizations:

Design Concepts	Visualization
2.1Overview Scatterplot with Box Selection Once rules are generated, they will be saved as data frame in the server. Each row represents one rule, which consists of the following variables: ruleID, Antecedent, Consequent, support, confidence, lift. The first visualization that users would see is an overview scatterplot. We plot all generated rules on a scatter plot where X axis is support and Y axis is confidence, colored by lift. Optimally, we would focus on those points at up-right corner of the plot and in darker red color, which is interpreted as high support, high confidence and high lift. This is a static scatterplot but we enable users to do box selection on it and they would see: 1) a zoomed-in interactive scatterplot on the right hand for them to further investigate those rules that they are more interested in; 2) a table of rule details for them to browse details of all selected rules; 3) a rule network to visualize all rules selected and the relationship between all related items. To achieve this function, we add in one parameter in this plotOutput called “function”: This “brush” function would read where the users click, drag, and release on the plot, show it on the plot as a box selection and store the box’s value range on X and Y axis. The box’s value range thus serve as constraints on support and confidence to subset the rules for further visualization.
2.2 Zoomed-in Scatterplot Our zoomed-in scatterplot is an interactive one, built using “ggplot2” wrapped in “plotly”. Within the X, Y value range defined by box selection, users can still further zoom-in or zoom-out on the zoomed-in scatterplot. More importantly, once the user hovers on any point, a tooltip will pop out showing the support, confidence and lift of this rule, and its lhs and rhs content, which gives the user a clearer picture what are there inside the rules they selected.
2.3 Rule Details We also provide a data table below the zoomed-in scatterplot, which gives rule details for users to have a full-screen view of selected rules. This is done by using “brushedPoints” function which subset data based on brushed points.

3.Network Visualizations

The network visualizations were built using package “vizNetwork” and “igraph”. The trick to create a network visualization from the association rules is to first visualize the rules using the default network visualization provided by the “arulesviz” package (graph method = “graph”). The arulesviz uses “igraph” package to visualize the rules into network diagram. Next, using the “get.data.frame” function from igraph package will automatically create two data frames, one for the nodes and one for the edges. VizNetwork packages could then be used to create the network visualizations. Viznetwork provides additional customization functionalities to the netowork diagrams like highlight the nearest nodes or allow the users to select certain nodes to zoom in.

It shall be highlighted that the vertices dataframe created by the “get.data.frame” function has a little bug. The quality measures are not correctly matched to the rules, with the last rule always showing having blank quality measures. This affects the visualization if we wish to reflect the interestingness of the rules in the network diagram, eg size the rules nodes by “lift”. The bug was corrected by adjusting the dataframes back so that the quality measures are in line with the individual rules. We also created additional columns to colour the rules and the items respectively to better differentiate them in the network diagrams.

The table below explains how to interpret the network visualizations.

Descriptions	Visualization
Grey nodes: individual rules Size of the grey nodes: indicating the interestingness of the rule, the bigger the better. By default, it is set to “lift” which can be changed to “support” or “confidence” Red nodes: indicating individual items, they are linked to the rules by edges with arrows
2.2 Zoomed-in Scatterplot Clicking on any rule, the items linking to that rule will be highlighted and the rest will be faded. The arrows show the direction of influences. LHS (antecedent) items will have arrows pointing to the rule nodes, indicating they are the factors of influences. RHS(consequent) items will have the arrows pointing from the rule nodes, indicating they are the outcome.