55 skills found · Page 1 of 2
chuanconggao / PrefixSpan PyThe shortest yet efficient Python implementation of the sequential pattern mining algorithm PrefixSpan, closed sequential pattern mining algorithm BIDE, and generator sequential pattern mining algorithm FEAT.
geroldmeisinger / ComfyUI Outputlists CombinerHandle multiprompts and images within one run. Quick OutputLists from spreadsheet, JSON, multiline text, numberranges for sequential processing. Combinations of lists and prompts. Load any file with metadata and glob patterns. Native XYZ-GridPlots, with supergrids and videogrids. Inspect COMBO in LoRA loader, sampler etc. Strings with placeholders.
lindermanlab / PPSeq.jlNeyman-Scott point process model to identify sequential firing patterns in high-dimensional spike trains
LoLei / Spmf PyPython SPMF Wrapper 🐍 🎁
eonu / SequentiaScikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.
mpatacchiola / Y AEOfficial Tensorflow implementation of the paper "Y-Autoencoders: disentangling latent representations via sequential-encoding", Pattern Recognition Letters (2020)
jacksonpradolima / Gsp PyGSP (Generalized Sequence Pattern) algorithm in Python
fandu / Maximal Sequential Patterns MiningA handy Python wrapper of the famous VMSP algorithm for mining maximal sequential patterns.
skrusche63 / Spark FsmThis project provides sequential pattern mining for Apache Spark. The algorithms are based on the work of Philippe Fournier-Viger and comprise his SPADE and TSR algorithm. This enables to perform sequential pattern and also sequential rule mining.
nphdang / Sqn2VecUnsupervised Sequence Embedding via Sequential Patterns
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
mathchi / Customer Segmentation With RFM AnalysisContext A real online retail transaction data set of two years. Content This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers. Column Descriptors InvoiceNo: Invoice number. Nominal. A 6-digit integral number uniquely assigned to each transaction. If this code starts with the letter 'c', it indicates a cancellation. StockCode: Product (item) code. Nominal. A 5-digit integral number uniquely assigned to each distinct product. Description: Product (item) name. Nominal. Quantity: The quantities of each product (item) per transaction. Numeric. InvoiceDate: Invice date and time. Numeric. The day and time when a transaction was generated. UnitPrice: Unit price. Numeric. Product price per unit in sterling (£). CustomerID: Customer number. Nominal. A 5-digit integral number uniquely assigned to each customer. Country: Country name. Nominal. The name of the country where a customer resides. Acknowledgements Here you can find references about data set: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II and Relevant Papers: Chen, D. Sain, S.L., and Guo, K. (2012), Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining, Journal of Database Marketing and Customer Strategy Management, Vol. 19, No. 3, pp. 197-208. doi: [Web Link]. Chen, D., Guo, K. and Ubakanma, G. (2015), Predicting customer profitability over time based on RFM time series, International Journal of Business Forecasting and Marketing Intelligence, Vol. 2, No. 1, pp.1-18. doi: [Web Link]. Chen, D., Guo, K., and Li, Bo (2019), Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, 28-31 Oct, 2019. Laha Ale, Ning Zhang, Huici Wu, Dajiang Chen, and Tao Han, Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network, IEEE Internet of Things Journal, Vol. 6, Issue 3, pp. 5520-5530, 2019. Rina Singh, Jeffrey A. Graves, Douglas A. Talbert, William Eberle, Prefix and Suffix Sequential Pattern Mining, Industrial Conference on Data Mining 2018: Advances in Data Mining. Applications and Theoretical Aspects, pp. 309-324. 2018. Inspiration This is Data Set Characteristics: Multivariate, Sequential, Time-Series, Text
luguoqing / Diff FSPMMining Frequent Sequential Patterns under Differential Privacy
ledormeurduval / PrefixSpanSequential Pattern Mining - PrefixSpan - Fitting the implementation of Tianming Lu with the Spark MLlib Python API
alessandroaere / ERMinerERMiner: Sequential Pattern Mining algorithm for Sequential Rules generation and Event Prediction
GuillaumeDD / DialignAutomatic and generic measures of verbal alignment in dyadic dialogue based on sequential pattern mining at the level of surface of text utterances
fahad19 / Glob RunRun multiple commands by glob patterns sequentially.
takmanx / Pyprefixspanpyprefixspan - Python implementation for the algorithm PrefixSpan (Prefix-projected Sequential Pattern mining).
hoangsonww / Earthquake R Analysis🌏 A project for visualizing and analyzing global earthquakes (M ≥ 2.5, last 30 days) using a single R script that automates data download, cleaning, and plotting. Generates 15 sequential plots covering spatial, temporal, and statistical patterns, including regression analysis of magnitude vs. depth.
chuanconggao / PrefixSpan ScalaThe shortest yet efficient implementation of the famous sequential pattern mining algorithm PrefixSpan in Scala.
