Shopping Cart Analyzer - Manual (1/5)

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1. Purpose

Shopping Cart Analyzer is designed to analyze purchases made for example in a supermarket. It applies a new technology for searching associative rules based on brand new approach and proprietary algorithms. The system gives a possibility of finding high-accuracy associations in the initial set according to the given data element. This forms a set with a high support even in large data sets.

2. The Market Basket Analysis Task

Market Basket contains a set of commodities and/or services purchased by a Buyer within one single purchase. They are, for example, results of the Buyer┬Тs visiting supermarket, grocery, an interactive purchase in an Internet online store, etc. Registering business operations within the whole period of their activities, different companies offering commodities or services accumulate large collections of such transactions (databases).

One of the most common tasks for statistic analysis of these databases is to find commodities and sets of items that are concurrently encountered in many transactions. Buyer's behavior patterns revealed as a result of this analysis are generally characterized by a list of commodities included in the set and the amount of transactions containing these sets. Trade companies use these patterns in order to allocate commodities in stores in a more efficient way, to change the structure of pages in commodity catalogues and web pages, to form packages of services encountered together and so on. A set consisting of i-commodities is called i-itemset. The percentage of transactions having this set is called "ratio" of the set. It is considered that for this set to be of a certain interest its support should be higher than the threshold established by the user; such sets are called frequent.

For an itemset a "confidence" characteristic is often used; it is connected with the set revelation accuracy using a certain algorithm. The accuracy is often determined with regards to one of the set items. It equals to a probability of some i-element joining the set with the obligatory inclusion of i - 1 elements into the set. The higher the chosen set "confidence", the more significance has the concerned set for the real practice. Length of i-set is an important characteristic as well.

3. General Information of the Shopping Cart Analyzer Operation

3.1. Initial Data Format

Initial data should be represented in one of the two types that Shopping Cart Analyzer identifies automatically:

3.1.1. List of transactions, where the commodities included in transaction are separated from each other by some separator. See an example data fragment below:

SMS,MMS,Internet,Dating,Entertainment
Mobile audio,Mobile commerce,SMS,Information
Information,Mobile audio,Call waiting
Conference call,Information,Mobile payments,Mobile commerce
Entertainment,Internet,Positioning,Mobile video
SMS,Call waiting,Internet,Dating,Entertainment
Games,Mobile commerce,SMS,Information
Information,Mobile audio,Call waiting
Conference call,Information,Mobile payments,Mobile commerce
Entertainment,Internet,Positioning,Mobile video
SMS,Call waiting,Internet,Dating,Entertainment
Games,Mobile commerce,SMS,Information
Information,Mobile audio,Call waiting

3.2. Operations Sequence

Four main parameters are set in the Shopping Cart Analyzer after reading of the initial data:

1. A_i commodity, with which associations are to be found.
2. The transaction number (line in the Data Table), for which the most complete association with the given accuracy is searched.
3. Desirable level of the association error (accuracy).
4. Minimal level of the transaction support with the given item.

On the first stage of the system operation user selects a desirable commodity A_i (as a rule, most frequently purchased) and sets a planned error level for the associative rule. Then the system automatically finds the first association with A_i product, for which confidence and support are calculated. During the next stage the system selects the most saturated with A_i commodity purchases transaction that has not been covered earlier by the first association, and finds the second association with A_i product for it. The two obtained associations together cover larger number of transactions than they could do separately. After that the same procedure continues in a similar way for transactions that have not been covered earlier till all associations with A_i product satisfying the given parameters are found.

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