Formulation of a Taxonomy Aware Catalog Integration Problem

The program created category similarity analysis which compared the products with similar category, a sales rank comparison of two or more products which views the sales rank and checks for similar ranks of the purchase and sees if one is preferred over the other. The program also checked for title similarity, average rating, and reviews of similar

In this module, the class label for the testing data is predicted. The n – dimensional feature vector for the testing data is converted from query tree of testing data in the manner similar to the data pre – processing phase. The SQLIA classifier determines the new testing feature vector is normal or malicious, by using optimized SVM classification model.

AdaBoost and C4.5 decision tree. The first classifier used is Naïve Bayes it is a probabilistic classifier

Usually the data mining analysis is done by grouping commonly co-occuring things (Associations), discovering time-ordered events (Sequences), anticipating future occurences (Predictions), identifying natural groupings of items (Clusters) and finally, by uncovering generalizations to help classify items (Classification). These different type of mining usually take a lot of time and a good understanding of the business and

The process and choice of classifying information is very important. Data of different types have different values to the owner of the information. Some data may be of more value or critical importance than other data. Certain information is therefore valuable, and if lost could cause great financial loss.

Markets for any product comprises several segments of consumers, each with different needs and wants. Market segmentation can be categorised in the following manner:

A new self-configuring and adaptive schema matching system has been proposed. It uses a combination of schema and matrix features as well as different matching rules to construct and adapt matching processes automatically. This framework offers some advantages like: correct methods for dealing with schemas that are semantically rich like ontologies; extensible matchers; and semi-automated derivation of the rules that are constructed for matching based on already mapped sources like identifying constraints, synonyms etc. For diverse match problems this approach returns good results as well as it is a good competitor of manually tuned approaches. It can be further enhanced to support more complicated

The machine learning block uses the random forest algorithm to analyze the data that is being fetched by the learning agent after which it analysis report is sent back where in it is used to create the diagnosis function which is the required output

Consolidate and integrate enterprise data by subject area in a relational store at the lowest level of

In today’s business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer.

From figure 5 and 6, data mining techniques compliment with queries helps to find hidden relationships in the data marts in order to find customer preferences and suggest customized advertisements (Faria 2012, p. 255-256). As a result of acquisition, expansion and business nature, Almost 21 requires to do substantial analytical works in order to source complex data relationships within different customer segments such as the relation of spending behavior against age groups and the relation of product sales against age groups. Therefore, BI applications aims to help supply chain planners to implement product mix decisions which refers to the process of choosing the right product options to the customers for sale (Chowdhury & Das 2012, p. 219). Additionally, BI applications advance the performance of Almost 21’s SCM and CRM. From a SCM perspective, the transparency of data, the availability of data mining and OLAP in data warehouse help to construct a more customized product and strengthen the supplier-suppler relationship. Hence, the BI applications facilitate the coordination and decision-making process in the supply-chain network, which in turn benefits the end-users. For example, Zara is a successful firm which had short lead times for new fashion idea because of its comprehensive data analysis, design and product management (Walter 2013). Furthermore, the BI applications help companies to

Since the invention of computers, they are used to process raw data, analyse and display converted data into useful information to the end users. Entering the information about an item requires repeated data entry which can be burdensome, costly and error-prone. Because of this, many

Data mining helps marketing companies build models based on historical data to predict who will respond to the new marketing campaign, for example online marketing.”

To look at one product of a company by sensible recommendation of product positioning, targeting and relevant marketing segmentation bases.

To look at one product of a company by sensible recommendation of product positioning, targeting and relevant marketing segmentation bases.