Aasaaska afraad ee kacaanka warshaduhu wuxuu inta badan ku xirnaan doonaa Xogta^(Data) iyo Isku xirnaanta^{(Connectivity)} . Adeegyada Falanqaynta ee^{(Analysis Services)} awood u leh inay horumariyaan ama abuuraan xalalka macdanta xogta ayaa door muhiim ah ka ciyaari doona arrintan. Waxay kaa caawin kartaa falanqaynta iyo saadaalinta natiijooyinka habdhaqanka iibsashada macaamilka ee lagu beegsanayo kuwa iman kara. Xogtu^(Data) waxay noqon doontaa kheyraad dabiici ah oo cusub habka soo saarista macluumaadka la xiriira xogtan aan la kala soocin waxay qaadan doontaa muhiimad aad u weyn. Sidan oo kale, fahamka saxda ah ee ereyga - Macdanta Xogta^{(Data Mining)} , habraacyadeeda, iyo codsigeeda ayaa naga caawin kara horumarinta hab dhammaystiran oo ku saabsan ereygan.

Aasaaska Macdanta Xogta^{(Data Mining Basics)} iyo Farsamadeeda

Macdanta xogta, oo sidoo kale loo yaqaano Aqoonta Discovery in Data^{(Knowledge Discovery in Data)} ( KDD ) waxay ku saabsan tahay raadinta dukaamada waaweyn ee xogta si loo ogaado qaababka iyo isbeddellada ka baxsan falanqaynta fudud. Tani, si kastaba ha ahaatee, ma aha xal hal-tallaabo ah ee waa geeddi-socod dhowr tallaabo ah waxaana lagu dhammeeyaa heerar kala duwan. Kuwaas waxaa ka mid ah:

1] Xog ururin iyo Diyaarin

Waxay ka bilaabataa xog ururin iyo habaynteeda habboon. Tani waxay kaa caawinaysaa in ay si weyn u wanaajiso fursadaha helitaanka macluumaadka lagu ogaan karo iyada oo loo marayo macdanta xogta

2] Dhismaha Modelka iyo Qiimaynta

Talaabada labaad ee habka macdan qodista xogta waa adeegsiga farsamooyinka qaabaynta ee kala duwan. Kuwaas waxaa loo isticmaalaa in lagu qiyaaso cabbirrada si loo qiimeeyo qiimaha ugu fiican. Farsamooyinka la shaqeeyaa waxay inta badan ku xidhan yihiin awoodaha falanqaynta ee looga baahan yahay si wax looga qabto baahiyaha urureed iyo in lagu gaadho go'aan.

Aynu si kooban u baarno qaar ka mid ah farsamooyinka macdanta xogta. Waxaa la ogaaday in ururada intooda badani ay isku daraan laba ama in ka badan oo ah farsamooyinka macdanta xogta si ay u sameeyaan hab ku habboon oo buuxiya shuruudaha ganacsigooda.

Akhriso^(Read) : Waa maxay Xogta Weyn?^{(What is Big Data?)}

Farsamooyinka Macdanta Xogta

1. Ururka -  ^{(Association – )}Ururku^{(Association)} waa mid ka mid ah farsamooyinka macdanta xogta si weyn loo yaqaan. Tan hoosteeda, qaab ayaa lagu kala saaraa iyadoo lagu salaynayo xidhiidhka ka dhexeeya alaabada isla wax kala iibsiga. Sidaa darteed^(Hence) , waxaa sidoo kale loo yaqaan farsamada xiriirka. Tafaariiqlayaasha caanka ah waxay ku tiirsan yihiin farsamadan si ay u baaraan caadooyinka iibka / dookhooda. Tusaale ahaan, marka ay la socdaan caadooyinka wax iibsiga ee dadka, tafaariiqleyaashu waxay ogaan karaan in macaamiishu uu had iyo jeer iibsado kareem marka ay iibsanayaan shukulaatada, sidaas darteed waxay soo jeedinayaan in marka xigta ee ay iibsanayaan shokolaatada ay sidoo kale rabaan inay iibsadaan kareem.
2. Kala^{(Classification)} -saarid - Farsamadan xogta macdaneed waxay kaga duwan tahay kuwa kor ku xusan habka ay ku saleysan tahay barashada mashiinka waxayna isticmaashaa farsamooyinka xisaabta sida barnaamijka tooska^(Linear) ah, geedaha go'aanka , ^(Decision)Neuralshabakad. Kala soocida, shirkaduhu waxay isku dayaan inay dhisaan software ka baran kara sida loo kala saaro walxaha xogta kooxo. Tusaale ahaan, shirkaddu waxay qeexi kartaa kala-soocida codsiga "iyadoo la siinayo dhammaan diiwaannada shaqaalaha soo bandhigay inay iska casilaan shirkadda, saadaaliyaan tirada shakhsiyaadka ay u badan tahay inay iska casilaan shirkadda mustaqbalka." Xaaladdan oo kale, shirkaddu waxay u kala saari kartaa diiwaannada shaqaalaha laba kooxood oo kala ah "ka tag" iyo "joog". Waxay markaa isticmaali kartaa software-ka macdanta xogta si ay u kala saarto shaqaalaha kooxo gaar ah oo hore loo abuuray.
3. Koox- kooxeed^(Clustering) - Kala duwan^(Different)Walxaha muujiya sifooyin isku mid ah ayaa la isugu geeyaa hal koox iyadoo loo marayo otomaatiga. Kutlo badan oo noocan oo kale ah ayaa loo abuuray sida fasallo iyo walxo (oo leh sifooyin isku mid ah) ayaa loogu dhejiyaa si habboon. Si aan tan si ka sii wanaagsan u fahanno, aan tixgelinno tusaale maareynta buugaagta ee maktabadda. Maktabadda dhexdeeda, buugaagta aad u badan ayaa si buuxda loo habeeyey. Alaabta isku nooc ah ayaa la wada taxay. Tani waxay noo sahlaysaa inaan helno buug aan daneyno. Sidoo kale, annagoo adeegsanayna farsamada kooxaynta, waxaan ku haysan karnaa buugag ay isku mid yihiin hal koox oo aan u dhiibno magac ku habboon. Sidaa darteed, haddii akhristuhu uu raadinayo inuu qabsado buug ku habboon xiisihiisa, kaliya waa inuu aadaa shelfas halkii uu ka raadin lahaa dhammaan maktabadda. Haddaba, farsamada kooxaynta ayaa qeexda fasallada oo waxay gelisaa walxo fasal kasta,
4. Saadaasha^(Prediction) - Saadaasha waa farsamada macdanta xogta oo inta badan loo isticmaalo iyadoo lagu darayo farsamooyinka kale ee macdanta xogta. Waxay ku lug leedahay falanqaynta isbeddellada, kala-soocidda, isbarbardhigga qaabka, iyo xiriirka. Marka la falanqeeyo dhacdooyinkii hore ama dhacdooyinkii hore ee isku xigxiga waxa si badbaado leh loo saadaalin karaa dhacdo mustaqbalka. Tusaale ahaan, farsamada falanqaynta saadaasha waxaa loo isticmaali karaa iibka si loo saadaaliyo faa'iidada mustaqbalka haddii iibka loo doorto doorsoome madaxbannaan iyo faa'iidada doorsoome ku tiirsan iibka. Dabadeed, iyadoo lagu saleynayo iibka taariikhiga ah iyo xogta faa'iidada, qofku wuxuu sawiri karaa qalooca dib-u-celinta ku habboon ee loo isticmaalo saadaalinta faa'iidada.
5. Geedaha go'aanka^{(Decision trees)} - Geedka go'aanka dhexdiisa, waxaan ku bilaabeynaa su'aal fudud oo leh jawaabo badan. Jawaab kastaa waxay keentaa su'aal dheeraad ah si ay u caawiso kala saarista ama aqoonsiga xogta si loo kala saaro, ama si saadaal ah loo sameeyo iyada oo ku saleysan jawaab kasta. Tusaale ahaan, waxaan isticmaalnaa geedka go'aanka soo socda si aan u go'aamino haddii la ciyaaro cricket ODI iyo in kale : Xogta Macdanta Go'aanka Geedka^{(Data Mining Decision Tree)} : Laga bilaabo xididdada xididka, haddii saadaasha cimiladu saadaaliso roobka markaa, waa inaan iska ilaalinaa ciyaarta maalinta. Haddii kale, haddii saadaasha cimiladu ay caddahay, waa inaan ciyaarnaa ciyaarta.

Macdanta Xogta^{(Data Mining)} ayaa udub dhexaad u ah dadaalka falanqaynta ee warshado iyo culuum kala duwan sida isgaadhsiinta, caymiska^(Insurance) , waxbarashada^(Education) , wax soo saarka^{(Manufacturing)} , bangiyada^(Banking) iyo tafaariiqda^(Retail) iyo in ka badan. Sidaa darteed, in la helo macluumaad sax ah oo ku saabsan arrintan waa lagama maarmaan ka hor inta aan la isticmaalin farsamooyinka kala duwan.

What is Data Mining? Basics and its Techniques.

The fоundatiоn of the fourth industrial revolution will largely depend upon Data and Connectivity. Analysis Services capable of developing or creating data mining solutions will play a key role in this regard. It could assist in analyzing and predicting outcomes of customer purchasing behavior for targeting potential buyers. Data will become a new natural resource and the process of extracting relevant information from this unsorted data will assume immense importance. As such, a proper understanding of the term – Data Mining, its processes, and application could help us in developing a holistic approach to this buzzword.

Data Mining Basics and its Techniques

Data mining, also known as Knowledge Discovery in Data (KDD) is about searching large stores of data to uncover patterns and trends that go beyond simple analysis. This, however, is not a single-step solution but a multi-step process and is completed in various stages. These include:

1] Data gathering and Preparation

It starts with data collection and its proper organization. This helps in significantly improving the chances of finding the information that can be discovered through data mining

2] Model Building and Evaluation

The second step in data mining process is the application of various modeling techniques. These are used to calibrate the parameters to optimal values. Techniques employed largely depend on analytic capabilities required to address a gamut of organizational needs and to arrive at a decision.

Let us examine some data mining techniques in brief. It is found that most organizations combine two or more data mining techniques together to form an appropriate process that meets their business requirements.

Read: What is Big Data?

Data Mining Techniques

1. Association – Association is one of the widely-known data mining techniques. Under this, a pattern is deciphered based on a relationship between items in the same transaction. Hence, it is also known as the relation technique. Big brand retailers rely on this technique to research customer’s buying habits/preferences. For example, when tracking people’s buying habits, retailers might identify that a customer always buys cream when they buy chocolates, and therefore suggest that the next time that they buy chocolates they might also want to buy cream.
2. Classification – This data mining technique differs from the above in the way that it is based on machine learning and uses mathematical techniques such as Linear programming, Decision trees, Neural network. In classification, companies try to build software that can learn how to classify the data items into groups. For instance, a company can define a classification in the application that “given all records of employees who offered to resign from the company, predict the number of individuals who are likely to resign from the company in future.” Under such a scenario, the company can classify the records of employees into two groups that namely “leave” and “stay”. It can then use its data mining software to classify the employees into separate groups created earlier.
3. Clustering – Different objects exhibiting similar characteristics are grouped together in a single cluster via automation. Many such clusters are created as classes and objects (with similar characteristics) are placed in it accordingly. To understand this better, let us consider an example of book management in the library. In a library, the vast collection of books is fully cataloged. Items of the same type are listed together. This makes it easier for us to find a book of our interest. Similarly, by using the clustering technique, we can keep books that have some kinds of similarities in one cluster and assign it a suitable name. So, if a reader is looking to grab a book relevant to his interest, he only has to go to that shelf instead of searching the entire library. Thus, the clustering technique defines the classes and puts objects in each class, while in the classification techniques, objects are assigned into predefined classes.
4. Prediction – The prediction is a data mining technique that is often used in combination with the other data mining techniques. It involves analyzing trends, classification, pattern matching, and relation. By analyzing past events or instances in a proper sequence one can safely predict a future event. For instance, the prediction analysis technique can be used in the sale to predict future profit if the sale is chosen as an independent variable and profit as a variable dependent on sale. Then, based on the historical sale and profit data, one can draw a fitted regression curve that is used for profit prediction.
5. Decision trees – Within the decision tree, we start with a simple question that has multiple answers. Each answer leads to a further question to help classify or identify the data so that it can be categorized, or so that a prediction can be made based on each answer. For example, We use the following decision tree to determine whether or not to play cricket ODI: Data Mining Decision Tree: Starting at the root node, if the weather forecast predicts rain then, we should avoid the match for the day. Alternatively, if the weather forecast is clear, we should play the match.

Data Mining is at the heart of analytics efforts across a variety of industries and disciplines like communications, Insurance, Education, Manufacturing, Banking and Retail and more. Therefore, having correct information about it is essential before apply the different techniques.

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