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dyneth02 / FDM LabsComprehensive machine learning framework for genomic analysis and predictive modeling. This repository showcases advanced classification and clustering techniques using XGBoost, CatBoost, LightGBM, and RandomForest to identify genetic disorders. Includes association rule mining with Apriori and unsupervised geographical clustering via KMeans.
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]
gkrishna9790 / Market Basket AnalysisMarket Basket Analysis using Apriori algorithm & Association rules
coorty / Apriori Agorithm PythonAn Effectively Python Implementation of Apriori Algorithm for Finding Frequent sets and Association Rules
CPSC4310 / Grid Based ClusteringA grid-based clustering algorithm for mining quantitative association rules.
shashir / Spark AprioriAssociation rule mining using Apache Spark.
diogoalexandrefranco / Cl Association RulesFor now, an implementation of the apriori algorithm to mine association rules in Common Lisp.
TheophileBERTELOOT / MOEA ARMMulti Objectives Evolutionnary Algorithm - Association rules mining
cpearce / Arm RsAssociation rule mining via FPGrowth in Rust
nousheentabassum / Machine LearningCollection of Machine Learning, Statistics, and Data Science notebooks covering EDA, hypothesis testing, regression, classification, clustering, ensemble learning, and association rule mining using Python.
Sara12-2 / Market Basket Analysis By Apriori AlgorithmThis project performs Market Basket Analysis using the Apriori algorithm to discover frequent itemsets and association rules from transactional data.
beerda / Lfllinguistic fuzzy logic algorithms: mining for linguistic fuzzy association rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy rule-based ensemble (FRBE)
XuJin1992 / The Research And Implementation Of Data Mining For Geological DataData mining and knowledge discovery, refers to discover knowledge from huge amounts of data, has a broad application prospect.When faced with geological data, however, even the relatively mature existing models, there are defects performance and effect.Investigate its reason, mainly because of the inherent characteristics of geological data, high dimension, unstructured, more relevance, etc., in the data model, indexing structure knowledge representation, storage, mining, etc., is far more complicated than the traditional data. The geological data of the usual have raster, vector and so on, this paper pays attention to raster data processing.Tobler theorem tells us: geography everything associated with other things, but closer than far stronger correlation.Spatial correlation characteristics of geological data, the author of this paper, by establishing a spatial index R tree with spatial pattern mining algorithms as the guiding ideology, through the raster scanning method materialized space object space between adjacent relationship, transaction concept, thus the space with a pattern mining into the traditional association rules mining, and then take advantage of commonly used association rules to deal with some kind of geological data, to find association rules of interest. Using the simulation program to generate the geological data of the experiment, in the process of experiment, found a way to use R tree indexing can significantly speed up the generating spatial transaction set, at the same time, choose the more classic Apriori algorithm and FP - growth algorithm contrast performance, results show that the FP - growth algorithm is much faster than the Apriori algorithm, analyses the main reasons why the Apriori algorithm to generate a large number of candidate itemsets.In this paper, the main work is as follows: (1) In order to speed up the neighborhood search, choose to establish R tree spatial index, on the basis of summarizing the common scenarios to apply spatial indexing technology and the advantages and disadvantages. (2) Based on the analysis of traditional association rule mining algorithm and spatial association rule mining algorithm on the basis of the model based on event center space with pattern mining algorithm was described, and puts forward with a rule mining algorithm based on raster scanning, the algorithm by scanning for the center with a grid of R - neighborhood affairs set grid, will study data mining into the traditional data mining algorithm. (3) In the process of spatial index R tree insert, in order to prevent insertion to split after the leaf node, leading to a recursive has been split up destroy the one-way traverse, is put forward in the process of looking for insert position that records if full node number is M (M number) for each node up to insert nodes, first to divide to avoid after layers of recursive splitting up, speed up the R tree insertion efficiency. (4) On the basis of spatial transaction set preprocessing, realize the Apriori algorithm and FP-growth algorithm two kinds of classic association rule mining algorithm, performance contrast analysis.
kliegr / ArcAssociation Rule Classification
sachinbiradar9 / MS AprioriMS-Apriori is used for frequent item set mining and association rule learning over transactional data.
omjego / AR Mining Hash TreeAssociation rule mining with Apriori Algorithm. Implemented in Python . Used hash trees to optimize Apriori's performance.
AlistairLR112 / EnglandCrimeAssociationsFinding Association Rules between Location, Crime Type and Crime Outcome of Crime in England
ShakeelRaja / Marketing AnalyticsMarketing analysis using RFM analaysis, unsupervised clustering for automatic customer segmentation and association rules
apwheele / GenrulesGenetic algorithm to create association rules for relative risk
kevalmorabia97 / FPARM Frequent Patterns And Association Rule MinerThis is an implementation of Apriori algorithm for frequent itemset generation and association rule generation. The GUI is made using JAVA FX or Cmd_Line version can be used
