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Its goal is to manipulate data into knowledge [15], [18]-[19], [31], [33]. Pattern extraction is an important process of any data mining technique and it refers to the relationships between subsets of data. Data mining use different families of computational, statistical and machine learning methods that include statistical analysis, decision trees, neural networks, rule induction and refinement, and graphic visualization among others, to exhaustively explore data to reveal complex relationships that may exist.

Although machine learning techniques have been available for a long time, the development of advanced and user friendly tools for business intelligence [25] has made data mining more attractive and practical for organizations. When these pattern extraction techniques are used correctly, they can be effective tools for extracting useful information from data [35].

The recent wide use of data mining has been due to several factors. The most obvious of these is the large amounts of data that organizations collect during operational transactions. In the early 90s, credit and insurance companies began using data mining as a means of detecting fraud [28]. Most organizations, irrespective of the industry type, have some form of operational process in which they collect large amounts of data. For example, the retail industry has been using data mining techniques for years to predict what their customers are likely to purchase.

The electronic commerce industry was one of the latest to use data mining technology [18]. Electronic commerce is the use of information and communication technologies through the Internet platform to share business information, keep business relationships, and conduct business transactions.

Data Mining

In electronic commerce, different data mining techniques can be used for many purposes. For example, in sales promotion the marketing staff may want to find out which products their customers are more likely to buy together. This information will allow them to place these items in a sales bundle in order to increase revenue [2], [31]. The use of Web log data permits to understand users' behavior. This data contains information about users' access and may show potential patterns in their behavior, and identify potential customers of electronic commerce.

This area of data mining has given rise to Web mining, a technique that can be subdivided into Web content mining; Web structure mining; and Web usage mining [7], [24]. These techniques are also used to extract useful information from Web documents or Web services [5] and are widely used in a variety of applications.

As we describe above, data mining and specifically web data mining technology plays an important role in electronic commerce. In recent years with the rapid growth of electronic commerce and the large amounts of data collected through operational transactions, data mining techniques are becoming more useful to discover and understand unknown customer patterns. In the following paragraphs we briefly describe some examples of the application of data mining in electronic commerce. Clustering or grouping electronic commerce customers with similar browsing behaviors permit the identification of their common characteristics, providing a better understanding of customers with the aim of giving them a more appropriate, and personalized service.

When a vendor knows the customer's needs and interests, they can work on providing a better service and keeping the customer relationship with the vendor. Electronic commerce organizations use web data mining to obtain reliable market and client feedback.

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For example, the information obtained may help organizations to undertake targeted marketing, decide advertisement positioning, and reduce operating costs. Techniques such as association rule mining permit the analysis of shopping cart data to improve the presentation or location of products. Those in charge of website and electronic commerce security may look at fraud detection, together with financial organizations such banks, and credit card companies, with the aim of detecting fraudulent use of their credit cards. Differences in the customer's spending pattern may suggest a possibility of fraud.

In intrusion detection the data mining algorithms may show that a certain sequence of events may indicate that there is an unauthorized access attempt by hackers [8], [31]-[32]. Understanding these patterns may help computer security personnel to prevent future intrusions. Some new areas of research have emerged and existing ones have increased their strength i. This has given life to more challenging problems [43] and specifically to Big Data Analytics.

The current electronic commerce systems used by the major Internet organizations such as Google, Amazon, Facebook, etc.

Kundrecensioner

They include highly scalable electronic commerce platforms, product recommender systems, social media platforms, and make use of web data that is less structured and that usually is composed of rich customer views and behavioral information [10]. The analysis of customer opinion in social media has used text analysis and sentiment analysis techniques [17], [30], [36]. Product recommender systems use customer segmentation and clustering, anomaly detection, graph mining, and more importantly association rule techniques [1]. The use of highly targeted searches and personalized recommendations has made possible long-tail marketing to reach millions of small niche markets [3].


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Machine learning is a mature area of computer science that researches how computers learn patterns and regularities in the data. Data mining, on the other hand, is performed by a human person with a specific goal. Usually, this person utilizes one or more pattern recognition algorithms that have been created in the machine learning field. This person deals with situations in which the data is available in impressive amounts and which probably possess some deficiencies such as missing data or high dimensionality when compared to the cardinality of the observation set. Data mining can be organized according to the different family of problems that it solves.

These problems include classifying items into previously known categories, grouping items according to their similarities, discovering association rules from transactions, identifying atypical data, and predicting a continuous dependent variable. This section will give a brief overview of these types of problems. In data mining applications one often assumes that the data is already in some sort of digital form something like a big spreadsheet. Here, one may want to predict the value of a particular attribute a particular column in the spreadsheet.

When this attribute, sometimes referred to as the class attribute, includes a finite number of discrete elements, we are in presence of a classification problem. In this type of problem, we build a mathematical model from the available data. This model receives the information of a novel instance whose class is unknown and produces an estimation of the category which it belongs to.

Our task is to perform this estimation as accurately as possible. In machine learning, classification is a form of supervised learning where instances or items are assigned to some predefined set of categories. In classification, the class memberships of all the training instances in the category are known in advance.

kinun-mobile.com/wp-content/2019-12-28/racel-how-to-set.php The fundamental idea behind classification, is that there is an underlying function f that relates the patterns and their respective categories. Unfortunately f is unknown to us and we want to estimate it by building a function g from the patterns and its categories, following the procedure specified by a learning algorithm. Recent applications of data classification include social network classification [38], credit scoring [37], fraud detection [21], web mining to predict e-commerce company success [34], among many more.

This problem is known as data clustering and represents a more challenging task from a learning perspective when compared to data classification. Here, our mathematical model receives the data without the class labels and we expect it to infer groups of elements just by merely examining their similarities. The output is an estimated class membership. In contrast to the classification problem where there is a set of possible classes known a priori, in the clustering problem different groups are created. The objective is to group similar instances in the same group, while at the same time, assign to distinct groups those elements which are different.

This type of learning is sometimes referred to as unsupervised learning because the function lacks of a teacher that tells the correct class label of a particular pattern. Interestingly enough, the human brain is particularly good at this task. For generations we have used this type of reasoning to distinguish between ripe fruit before collecting it or to build an entire taxonomy of the animal kingdom based on the observed characteristics: Nobody told us to categorize animals according to whether they produce milk or not!

Applications of data clustering in e-commerce include recommendation systems [40], search engines [23], etc. Classification is an example of supervised learning, assuming the knowledge of well-defined training sets with a clear specification of the identity of all the training samples. A distinct and intriguing learning paradigm that has emerged in the recent years is semi-supervised learning.

Advantages of Data Mining

This paradigm combines labeled and unlabeled instances simultaneously to perform classification [46]. This specific type of classifiers does not demand the specification of the class labels of every sample. Usually this type of learning appears in situations where many instances are available, but only few of them possess labels because the cost of acquiring them is high. One common way to learn in this context is to perform a clustering-like mechanism, assigning the training samples into different groups, and subsequently, a class label is assigned to each group using a small subset of the training instances whose class identities are known.

Given a clustering algorithm, , a set of labeled instances, X L , a set of unlabeled instances, X u , and a supervised learning algorithm, the Cluster-then-Label method works as follows [4]: First, we identify the clusters of the input manifold using the clustering algorithm. Secondly, we determine which of the labeled samples fall in each cluster. For each cluster we determine a decision boundary based on the supervised algorithm ,and the labeled samples assigned to that cluster, which, in turn, allows the prediction of the label of every cluster.

Finally, each uncategorized item is labeled according to the predicted class of the cluster in which it is contained.