Essay On Content Mining

This section discuss about the common traits or ideas observed in the three research topics. Although, each of the three articles discuss a unique idea, all of them are aimed at utilizing the web data to produce better results. Web data mining is a hot research topic in the current realm of big data. These papers discuss about the utilization of the valuable user generated data from the social media or the browser cookies to provide the best user experience in order to maintain the user interest in the company's product or to take effective decisions by an individual. All the three articles propose an idea to solution the problem stated, compared their results to the existing models and showed significant improvement.

The output of an association rule mining algorithm is a set of association rules respecting the user-specified minsup and minconf thresholds.

This is similar to apriori join step. In ith level combined items generates all possible ith level transaction for Ck (candidate itemsets) that using Lk-1 itemsets. Then frequency counts are discovered for each combination and generate linked list structure and allocate items in structure.

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

Before a data set can be mined, it first has to be ?cleaned?. This cleaning process removes errors, ensures consistency and takes missing values into account. Next, computer algorithms are used to ?mine? the clean data looking for unusual patterns. Finally, the patterns are interpreted to produce new knowledge.3

Intriguing examples from expansive databases. An average case in information mining is utilizing purchaser obtaining examples to anticipate which products to put near one another on racks, or to

web mining is a method of structure information from unstructured or semi-structured web data source. Corporations are using web mining as gears to collect data from the different website. The data is gathered and examined to design a website, which it can offer information from variety of websites. The business can raise the sales because the consumers have an aptitude to trace web users browsing manners to the mouse clicks. It allows a business to personalize facilities for the client.

(Ian, Frank & Hall 2011) The world testifies to the increasing gap between the development of data and our understanding of it. Data mining is defined as the analysis of big observational data sets to establish unsuspected relationships that summarize the data in a novel, understandable and useful way to the data owner (David, Heikki & Padhraic 2001). These relationships and summaries are referred to as models or patterns. Patterns comprise sets of co-occurring attribute values referred to as item sets or more complex patterns that include sequences, which consider explicit precedence relationships that are either positional or temporal, and graphs considering arbitrary relationships between points (Mohammed and Wagner 2014). Other types of additional summarised relationships include rules, clusters, tree structures among others.

As probably the most natural type of storing information is text and text mining is believed to have a commercial potential greater than that of data mining. The recent study indicated that 80% of a company’s information is including in text documents. Text mining, however, is also a more complex task as compare to data mining as it involves dealing with text data that are naturally unstructured and fuzzy. Text mining is a multidisciplinary area, involving information retrieval, text examination, information extraction, clustering, categorization, visualization, database technology, device learning, and data mining [2]. In Text Mining, patterns are extracted from natural language Textual Database. There are many methods of text mining. In general, the major approaches, based on the kinds of data they take as input,

YouTube is the one stop shop for a user’s video needs. Content ranges from funny home videos to vlogs to political commentary, with nothing left out in between. To keep access to the site free for viewers and creators, YouTube adopted a unique business model to maintain viewership and provide an incentive for creators to post content on the site. This model, though successful, brings an array of problems when deciding what content a creator can profit from. The method YouTube used to determine which videos could profit from advertising is unethical as it inhibits content creators’ ability to create entertaining videos to the site.

The Internet is a vast resource of data of different sorts: text, pictures, sound and video. With the continuous growth and abundance of data available on the Internet, the World Wide Web has become a huge repository of information. Web mining is the application of data mining to large web data repositories. Web Mining is the use of information mining techniques to automatically discover and extract information from web documents and

The web is highly dynamic; lots of pages are added, updated and removed everyday and it handle huge set of information hence there is an arrival of many number of problems or issues. Normally, web data is highly dimensional, limited query interfaces, keyword oriented search and limited customization to individual user. Due to this, it is very difficult to find the relevant information from the web which may create new issues. Web mining procedures are classification, clustering and association laws which are used to understand the customer behavior , evaluated a particular website by using traditional data mining parameter. Web mining process is divided into four steps; they are resources finding, data selection and pre-processing, generalization and analysis. Web measurement or web analytics are one of the significant challenged in web mining. The measurement factors are hits, page views, visits or user sessions and find the unique visitor regularly used to measures the user impact of various proposed changes. Large institutions and organizations archived usage data from the web sites. The main problem is that, detecting and/or preventing fraud activities. The web usage mining algorithms are more effective and accurate. But there is a challenge that has to be taken into consideration. Web cleaning is the most significant process but data cleaning becomes difficult when it comes to heterogeneous data. Maintaining accurateness in classifying the

Association rule mining was invented to extract patterns from transactional databases. As stated, an association rule is an method applied in the form X →Y, where X and Y are sets of items. Association rule mining finds all such conditions which

Data mining techniques can be mainly divided into three categories: Web structural mining, Web Content mining and web usage mining. Web structural mining is used to discover structure from data available on web like hyperlinks and documents. It can be helpful to the user for navigating within documents as mining can be done to retrieve intra and inter hyperlinks and DOM structure out of documents. Web Content mining can be used to extract information from the data available on web like texts, videos, images, audio files etc. Web usage mining is the application of data mining techniques to discover interesting usage patterns from web usage data, in order to understand and better serve the needs of web-based applications (Srivastava, Cooley, Deshpande, and Tan 2000). Websites or usage data takes user’s available information and browsing history, location of user etc. as input for mining information. Web usage mining can be further divided into three categories depending upon the type of data used for mining: web server logs, application server, application level logs. Web usage mining can be highly helpful in mining the data for web applications and thus helping development in fields like E-Commerce. It can be helpful to discover usage patterns from Web data, thus helping serve better the needs of Web-based applications. Web usage mining can be categorized into three different phases: Preprocessing, Pattern Discovery and Pattern Analysis. I believe, this

They showed that PSO approaches provides a better smaller intra-cluster distances and larger inter-cluster distances.