Data Mining techniques

Data Mining techniques ABSTRACT Competitive advantage requires abilities. Abilities are built through knowledge. Knowledge comes from data. The process of extracting knowledge from data is called Data Mining. Data mining, the extraction of hidden predictive information from large databases, is advance technique to help companies to highlight the most important information in their data warehouses. Data mining tools predicts future trends and behaviors. Data mining tools can answer business questions that traditionally were too time consuming to resolve. Data Mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and system as they are brought online. A Data warehouse is a platform that contains all of an organizations data in one place in a centralized and normalized form for deployment to users, to fulfill simple reporting to complicated analysis, decision support and executive level reporting/archiving needs. Physically, a data warehouse is a repository of information that businesses need to thrive in the information age. Analytically, a data warehouse is a modern reporting environment that provides users direct access to their data. In the information age, data warehousing is a powerful strategic weapon. Not only does it let organizations compete across time, it is also a rising tide strategy that can elevate the strategic acumen of all employees in a fields. This paper presents an overview of the data mining and warehousing, their basic definitions, how they are implemented and their pros and cons. DATA WAREHOUSING In todays competitive global business environment, it is crucial for organisations to understand and manage enterprise wide information for making timely decisions and respond to changing business conditions. With the receding economy, enterprises have changed their business focus towards customer orientation to remain competitive. Consequently, CRM tops their agenda and many companies are realizing the business advantage of leveraging one of their key assets data. Many research reports indicate that the amount of data in a given organization doubles every five years. As said earlier, the most fundamental aspect affecting the successful functioning of a business enterprise is the crucial decisions taken in this regard by the management. The cardinal entity that helps them in taking these decisions is the business critical information. This information can only be reliable and accurate if all the business related data is properly analyzed and further a thorough analysis is only possible if all the data affecting the enterprise is present at one place. The solution a data warehouse! Data Warehouse is a single, complete consistent store of data obtained from a variety of different sources made available to end users in what they can understand use in a business context. Today, data warehousing is one of the most talked-about business technologies in the corporate world. DATA MINING Data mining is a powerful new technology with great potential to help companies focus on the most important information in the data they have collected about the behavior of their customers and potential customers. It discovers information within the data that queries and reports cant effectively reveal. The amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. Raw data by itself, however, does not provide much information. In todays fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment. Fig: Data Explosion Data mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Data mining derives its name from the similarities between searching for valuable information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find where the value resides. Frequently, the data to be mined is first extracted from an enterprise data warehouse into a data mining database or data mart .The data mining database may be a logical rather than a physical subset of your data warehouse. DATA WAREHOUSING 1. DEFINITION: A data warehousing (DW) is a subject-oriented, integrated, time variant, non-volatile collection of data in support of managements decision making. A data warehouse is a relational database management system (RDMS) which offer organizations the ability to gather and store enterprise information in a single conceptual enterprise repository and is designed specifically to meet the needs of transaction processing systems. Data Warehousing deals with the organizing collecting data into database that can be searched mined for information through the use of intelligence solution. 2. CHARACTERISTICS OF A DATA WAREHOUSE 1) Subject-oriented The data in the database is organized so that all the data elements relating to the same real-world event or object are linked together; 2) Time-variant The changes to the data in the database are tracked and recorded so that reports can be produced showing changes over time; 3) Non-volatile Data in the database is never over-written or deleted once committed, the data is static, read-only, but retained for future reporting; and 4) Integrated The database contains data from most or all of an organizations operational applications, and that this data is made consistent. 3. ARCHITECTURE OF DATA WAREHOUSE The architecture for a data warehouse is given below. Building this architecture requires four basic steps: 1) Data are extracted from the various and internal source system files and databases. In a large organization there may be dozens or even hundreds of such files and databases. 2) The data from the various source systems are transformed and integrated before being loaded into the data warehouse. Transactions may be sent to the sources system to correct errors discover in data staging. 3) The data warehouse is a database organized for decision support. It contains both detailed and summary data. 4) User access the data warehouse by means of a variety of query languages and analytical tools. Results (e.g. prediction, forecast ) may be fed back to data ware house and operational databases. Information integrated in advance Stored in warehouse for direct querying and analysis Fig: Architecture of typical data warehouse ,and the querying and data-analysis support Architecture in Conceptual View Single-layer Every data element is stored once only Virtual warehouse Two-layer Real-time + derived data Most commonly used approach in industry today Three-layer transformation of real-time data to derived data really requires 2 steps 4. ISSUES IN BUILDING A WAREHOUSE 1) When and how gather data In a source driven architecture for gathering data, there data sources transmit new information. In a destination -driven architecture, the data warehouse periodically sends request for new data to the data source . 2) What Schema To Use Data sources that have been constructed independently are likely to have different schemas, part of data warehouse is schema integration, and to convert data to the integrated schema before they are stored .as a result data stored in warehouse are not just a copy of the data at the source 3) Data Cleansing The task of correcting and preprocessing data is called data cleansing data sources often deliver data with numerous minor inconsistencies that can be corrected. 4) How To Propagate Updates Updates on relations at the data sources must be propagated to data warehouse, if the relations at the data warehouse are exactly the same as those data source, propagation is straightforward 5) What To Summarize The data generated by the transaction-processing system may be too large to store online .we can maintain summary of data obtained by aggregation on a relation. 5. DATA WAREHOUSE MODEL Data warehousing is the process of extracting and transforming operational data into informational data and loading it into a central data store or warehouse. Once the data is loaded it is accessible via desktop query and analysis tools by the decision makers. The data warehouse model is illustrated in the following figure:. The materialized views contain summary data compiled from several data sources. The auxiliary views in the picture are not mandatory, and are used to contain additional information needed to support the synchronization of the materialized views with the data sources. Fig: Data ware house model The data within the actual warehouse itself has a distinct structure with the emphasis on different levels of summarization as shown in the figure below. Fig: Structure of data warehouse 6. STAGES IN IMPLEMENTATION A DW implementation requires the integration of implementation of many products. Following are the steps of implementation:- Step1: Collect and analyze the business requirements. Step2: Create a data model and physical design for the DW. Step3: Define the Data sources. Step4: Choose the DBMS and software platform for DW. Step5: Extract the data from the operational data sources, transfer it, clean it load into the DW model or data mart. Step6: Choose the database access and reporting tools. Step7: Choose the database connectivity software. Step8: Choose the data analysis and presentation software. Step9: Keep refreshing the data warehouse periodically. 7. DATA MARTS A data warehouse is the sum of all its data marts. A data mart is a complete pie-wedge of the overall data warehouse pie, a restriction of the data warehouse to a single business process or to a group of related business processes targeted toward a particular business group. Data marts can be customized for the end users ,and can present data in different formats for the end-users benefit. Data marts can employ OLAP , which is a method of database indexing that enhances quick access to data, specially in queries of data or viewing the data from many different aspects. DATA MINING 1. DEFINITION Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Data mining refers to using a variety of techniques to identify nuggets of information or decision-making knowledge in bodies of data, and extracting these in such a way that they can be put to use in the areas such as decision support, prediction, forecasting and estimation. The data is often voluminous, but as it stands of low value as no direct use can be made of it; it is the hidden information in the data that is useful. A data mining is also defined as A new discipline lying at the interface of statistics, data base technology, pattern recognition, and machine learning, and concerned with secondary analysis of large data bases in order to find previously unsuspected relationships, which are of interest of value to their owners. 2. PROCESS The data mining process can be divided into four steps: Data Selection Data Processing Data Transformation Data Mining Interpretation Evaluation Fig: Process used in data mining 3. WORKING While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought: Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials. Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities. Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining. Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumers purchase of sleeping bags and hiking shoes. 4. MODELS RELATED TO DATA MINING There are two types of model or modes of operation, which may be used to discover information of interest to the user. 1) Verification Model: The verification model takes input from the user and tests the validity of it against the data. The emphasis is with the user who is responsible for formulating the hypothesis and issuing the query on the data to affirm or negate the hypothesis. 2) Discovery Model: The discovery model differs in its emphasis in that it is the system automatically discovering important information hidden in the data. The data is sifted in search of frequently occurring patterns, trends and generalizations about the data without intervention or guidance from the user. 5. TECHNIQUES USED IN DATA MINING Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure. Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID). Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution. Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k  ³ 1). Sometimes called the k-nearest neighbor technique. Rule induction: The extraction of useful if-then rules from data based on statistical significance. 6. TWO STYLES OF DATA MINING There are two styles of data mining. Directed data mining is a top-down approach, used when we know what we are looking for. This often takes the form of predictive modeling, where we know exactly what we want to predict. Undirected data mining is a bottom-up approach that lets the data speak for itself. Undirected data mining finds patterns in the data and leaves it up to the user to determine whether or not these patterns are important. 7. POTENTIAL APPLICATIONS Data mining has many and varied fields of application some of which are listed below. Marketing: Identify buying patterns from customers Market basket analysis. Banking: Detect patterns of fraudulent credit card use Identify `loyal customers. Insurance and Health Care: Claims analysis, Predict which customers will buy new policies Identify fraudulent behavior. Transportation: Determine the distribution schedules Analyze loading patterns. CONCLUSION: Organizations today are under tremendous pressure to compete in an environment of tight deadlines and reduced profits. Legacy business processes that require data to be extracted and manipulated prior to use will no longer be acceptable. Instead, enterprises need rapid decision support based on the analysis and forecasting of predictive behavior. Data-warehousing and data-mining techniques provide this capability. A data warehouse is a modern reporting environment that provides users direct access to their data. A Data warehousing is the sum of all its Data Marts. Data warehousing strategy allows organizations to move from a defensive to an offensive decision-making position. The purpose of data warehouse is to consolidate and integrate data from a variety of sources and to format those data in a context for making accurate business decisions. Data mining offers firms in many industries the ability to discover hidden patterns in their data patterns that can help them understand customer behavior and market trends. The advent of parallel processing and new software technology enable customers to capitalize on the benefits of data mining more effectively than had been possible previously. REFERENCES 1) www.geekinterview.com/Interview-Questions/Data-Warehouse 2) www.datawarehousing.com/ 3) http://en.wikipedia.org/wiki/Data_warehouse 4) www.megaputer.com 5) www.research.microsoft.com

Positive Influence on Kids

Rhetorical Analysis Being a high school and college athlete, I have gained a lot of skills both physically and mentally. I have learned how to work with different personalities and learned how to adapt to them. Also, I have learned how to cope with winning and losing. I honestly believe that playing sports shaped me into the person I am today. I have great leadership skills, I interact well with others, I adapt well with new people and I do not give up on anything that I do.In his New York times editorial † Sports Teaches Kids Valuable Lessons,† Keener notes that while striving to win (on an organized ports team), children learn about teamwork and sportsmanship, two keys that can contribute to developing into a solid citizen. In sports, there is a bigger picture than just merely winning and losing. Once you have learned how to stay grounded and humble when you win, or even learning from a terrible loss, I believe you have overall gained a lifetime skill. Some play sports Just for the fun, while other play for the competitiveness of the game.Personally, I play for both reasons. Keener's argument is stating that these skills of teamwork, and sportsmanship may be acquired though ifferent realms other than sports, but they are enhanced through sports. He feels that they help children learn when the right time is to express those characteristics. Playing sports is a learning experience, you learn how to interact with different people, learn how to cope with losing, and learn how to persevere. In the editorial, Keener expresses that athletics build character and discipline, as well as teamwork and hard work.An organized sports team with members cooperating with one another to achieve a common goal, sport participation is a good way of further instilling the important principles of teamwork in a group. If one person does not do their part the whole team can and will suffer. If one individual dominates then the performance of others will be adversely affect ed in both areas the only way to achieve a victory is with a team working in harmony and on equal terms with one another. As with sport, a team may comprise multiple talents but if they don't work well together then the team will quickly fall apart.You win as a team and lose as a team. So with that, you have to learn, with your team, how fix the problem and be able to come together and win. Realistically, it is not guaranteed that you will win. Some teams go on with the worst records of not winning one game in a season. Keener explains, you learn how to get back up every time you get knocked down. With that, you will learn to thrive and compete through any recurring disappointments or setbacks. In the text, he illustrates how taking on a sport may take sacrifice.It can be transformation of your body from to fit the sport; there is a wide range of injuries, or even time away from daily activities. Sports should serve the purpose of general development, keeping the body strong and mai ntaining healthy life style. Keener noted that sports help the child keep up a healthy lifestyle. He notes that with athletes competing at a competitive level can lead to positive psychosocial, developmental and health benefits for girls. Research shows that sport participation and positive self-perceptions and also better success rates in the classrooms.Lastly, Keener takes a different approach by explaining how participating in sports gives athletes the ability to develop tight and lasting friendships. This is an experience that usually leaves them with lasting life long memories. There are some positive social benefits to sports participation that are easily overlooked by many. Keener tried to connect on a very person and emotional level. In high school, everyone wants a friend. Everyone wants to feel accepted or have that sense of belonging to something.Some kids may not have that sense of family at home, so they may Join a sport to fill in where their home life is lacking. Afte r reading the editorial, I still have the same feelings toward the topic. I absolutely agree with everything Keener had to say. Although some may say that there are some character traits that only parents can instill in their children. But there is something special to a child about having howing those skills off through some type of medium. Overall, Keener was very successful in his editorial. He clearly stated his argument and his claim.He factually and emotionally connected to different types of audiences, mostly athletes, future athletes and parents of athletes. What made it so successful was how he proved his claim through facts and thoughts, and also connecting with the right audience. It all made sense. He was able to connect with actual athletes and parents of athletes. He challenges the child to strive to be a better athlete by adhering to the life lessons learned. He also challenges the parents to challenge their child by encouraging them to get involved with a team.Keener touched upon different aspects ofa child's life that later on down the road requires usage of those skills they learned. Kids may never use their physical talents or skills again after high school or college, but one thing they will use is their leadership skills, teamwork and perseverance. Those are fundamental keys to being successful in the real world and work force.

Tuition Increase

It is widely accepted that the future prosperity of Canada rests on having a well-educated workforce. Yet, the cost to students of post-secondary education has risen rapidly over the last few years as government funding has dropped dramatically. Since the early 1980s, public funding of post-secondary education in Canada has gone down by 30 percent. In addition, across Canada, about 1. 1 million full-time students were enrolled in post-secondary institutions in 2001, but thousands have been turned away because of lack of space or they have not applied for admission because the cost of tuition is too high for them. Ontario has the second-highest tuition fees in the country. On average, tuition fees can cost an undergraduate student close to $5,000 per year. Over 80 per cent of Ontarians believe tuition fees are too high, even with the current freeze. More than 90 per cent of students voted to reduce tuition fees. Yet Ontario Premier Dalton McGuinty has announced that tuition fees will be increasing by up to 36 per cent over the next four years. Ontario's post-secondary system, which has 18 universities and 24 community colleges, receives the lowest per-student funding in the country. For the most part, reductions in university funding by both the federal and provincial governments explain higher university tuition. The federal cash transfer payments for education and training have been cut by $7 billion since 1993. In the 2000-2001 federal budget, only a $600 million increase was allocated for both health and education, with no real requirement that any of the money be spent on education. But the Minister of Finance was able to find $55 billion in tax cuts for corporations, the banks and wealthy Canadians. The money is available, but the wrong choices are being made. Students are now paying higher fees for a lower quality education – less access to libraries, less lab equipment, reductions in tenured teaching staff and support staff. Tuition fees are a regressive form of taxation. In 1997, Canadians spent 19 percent more on their household budget than in 1996 on education, but their total household spending did not increase. This does not mean that families are paying more for education, but it does mean that hey are sacrificing other expenses in order to meet the cost of an education. The government is attempting to deflect anger over tuition increases by pointing to changes in student aid. But the fact is the tuition fee increase over the next four years will effectively wipe out more than the student financial assistance investment to be phased in over the same period. In fact, for every dollar invested in student aid more than a dollar will be clawed back through tuition fee increases. In effect, students are borrowing to finance their own student aid program. A post-secondary education is now out of reach for poorer Canadians. Those who can get to college and university often end up with debts on graduation, which can range anywhere from $30,000 for a four-year undergraduate program to $60,000 for those doing graduate studies. Professional faculties can lead to much higher debts: annual tuition at the University of Toronto's medical school, for example, was more than $16,000 by 2003, and half of Canada's 16 medical schools were charging more than $10,000 a year. It can all add up to a six-figure bill after graduation – one observer suggested it's one reason why doctors are opting for specialties rather than family medicine because the pay for the former is much higher. It's been estimated that by 2020, a four-year university education will cost about $90,000. In addition to average annual increases, students are faced with deregulated fees. Deregulation of fees happens when a provincial government abandons all guidelines and legislation and lets individual institutions have complete control of tuition fee levels. Deregulation represents one of the most serious threats to accessibility of post-secondary education, since it always leads to massive tuition fee increases. Dentistry now costs up to $30,000 a year. Even with the maximum federal and provincial student loans and the maximum private student line of credit, this fee can't be met. Deregulation is not a new tuition fee structure, but the downloading of the cost of education onto the backs of students. Deregulation is not limited to university fees. Community college programs in Ontario vary from $1,700 to over $8,000 a year. It is wrong for the public to believe, and even worse for governments to promote the myth that fees can be raised without affecting accessibility. In a study released by the Maritimes Provinces Higher Education Commission in 1997, it was reported that â€Å"there are clear indications of a systemic social inequality affecting accessibility, with students from lower income backgrounds being disadvantaged in their ability to meet the financial demands of attending post-secondary institutions. â€Å"

Analysis Of The Poem Sunset - 1160 Words

Sunset Intro The year is 2075. A superior alien race called Crustinians have enslaved humanity to feed their ever-growing labor demands. They have prisoned every alive human into sectors based on their profession. I am in Sector – 13 since I am an optometrist. As a young man, I didn’t like the outdoors – hated everything from amusement parks to zoos. Yet, today, 10th April 2075, is the day I break out of captivity to watch for the very last time, the Sun dip in the sea from the Olympus Beach. It is fascinating what 15 years of imprisonment can do to you. Escape to Olympus Beach It was time. As I descended into the tunnels and made my way through them, I wondered if the sun was just as warm and golden as it was in the pre- Crustinian era. An hour and a half later, I climbed out of the manhole and it was everything I had imagined and more. The sun shone brightly, sparkling off the shimmering water which reflected like crystals. The light deluged over the grass and pierced even the darkest shadows. Heat rose from the ground in waves, creating the illusion of rippling water. The only escape from the scorching sunlight was beneath the pine trees that lined the shore, and even some dappled light streamed through the canopy, leaving tiny shapes on the ground in intricate patterns. I removed my shoes and felt the warm, soft sand under my feet as my cheeks became cold from the light breeze from the sea. It was the perfect opportunity to relax. At the Olympus Beach I have neverShow MoreRelatedPoem Analysis : Love Sunsets 1214 Words   |  5 PagesI used to love sunsets. I adored the drifting of the sun back beyond the horizon, vibrant colors dancing across the sky, giving off the last bit of light before disappearing into darkness beautiful, but not anymore. 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